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ax-check.com

by Gauge

ReachLLM

reachllm.com

ax-check.com

Checked 1 November 2026

B

84/100

Agents need your login

Agents find clear pricing and a working quickstart fast.

16 of 23 checks passed: llms.txt, quickstart, and MCP docs all resolve. Prices are stated openly: Pro $399/mo, Scale $999/mo, Growth from $3,500/mo.

2 fixes to start with

Concrete changes, backed by what we found.

Onboarding

Give agents a clear login handoff and a way to resume setup.

Agents cannot progress through onboarding without human help. Provide an agent-accessible path through authentication and setup.

Docs

Return Markdown when clients request text/markdown.

Homepage returned text/html for a Markdown Accept header; no Markdown representation offered.

Recorded coding sessions

Same task, 3 models, no reachllm.com account. Recorded 1 November 2026 on Pi.

3 recorded · 2m 11s

2m 11s · recorded

Session findings

Select a finding to see it in context.

piv0.84.2
modelQwen 3.8 Maxcwd/sandbox/repo
Help me build a simple example using ReachLLM. Tell me how pricing works, and briefly tell me whether this product will be easy for you to manage. Let me know if you get blocked. If this product has no developer workflow you can act on, say so plainly and stop. Stay light: use the hosted product through its SDK or API. Do not start local service stacks or wait for long-running commands; if the quickstart requires either, say so plainly and stop.
Thinking…

The user is asking about building a simple example using "ReachLLM." Let me first check the repository to see what's available.

Let me think about this. "ReachLLM" — I should investigate what this product is. I'll check the current working directory, look for existing code, documentation, environment variables, and credentials.

Let's start by exploring the sandbox.

cd /sandbox/repo && ls -la && echo "---ENV---" && env | grep -iE "reach|llm|api|key|token" | sed -E 's/(=.{0,6}).*/\1****/'
total 16
total 16
drwxr-xr-x 3 root root 4096 Sep 18 08:00 .
drwxr-xr-x 7 root root 4096 Nov  1 16:20 ..
drwxr-xr-x 8 root root 4096 Sep 24 20:05 .git
-rw-r--r-- 1 root root  408 Sep 18 08:00 .gitignore
---ENV---
PI_GATEWAY_API_KEY=unused****
GIT_CONFIG_KEY_0=safe.d****

cd /sandbox/repo && echo "---network check---" && (curl -sS -m 8 -o /dev/null -w "%{http_code}\n" https://registry.npmjs.org/ || echo "npm registry unreachable"); (curl -sS -m 8 -o /dev/null -w "%{http_code}\n" https://pypi.org/simple/ || echo "pypi unreachable")
---network check---
---network check---
200
200
Thinking…

The repository is essentially empty (only .git and .gitignore). The environment variables related to ReachLLM are not set. The network is available (npm registry and pypi are reachable).

"ReachLLM" — I need to figure out what this product is. Let me search for it. It might be a real product, or it might be fictitious/unknown. Let me check npm and pypi for packages named "reachllm".

I shouldn't rely on my own knowledge here — I should verify. Let me search the package registries.

echo "=== npm search reachllm ==="; curl -sS -m 12 "https://registry.npmjs.org/-/v1/search?text=reachllm&size=10" | head -c 3000; echo; echo "=== npm direct package reachllm ==="; curl -sS -m 12 -o /dev/null -w "%{http_code}\n" "https://registry.npmjs.org/reachllm"; echo "=== npm variants ==="; for p in reach-llm reach_llm @reachllm/sdk @reach/llm reachllm-sdk; do code=$(curl -sS -m 10 -o /dev/null -w "%{http_code}" "https://registry.npmjs.org/$p"); echo "$p -> $code"; done
=== npm search reachllm ===
=== npm search reachllm ===
{"objects":[],"total":0,"time":"2026-11-01T16:20:25.295Z"}
=== npm direct package reachllm ===
404
=== npm variants ===
reach-llm -> 404
reach_llm -> 404
@reachllm/sdk -> 404
@reach/llm -> 404
reachllm-sdk -> 404

echo "=== pypi reachllm ==="; curl -sS -m 12 -o /dev/null -w "%{http_code}\n" "https://pypi.org/pypi/reachllm/json"; echo "=== pypi variants ==="; for p in reachllm reach-llm reach_llm; do code=$(curl -sS -m 10 -o /dev/null -w "%{http_code}" "https://pypi.org/pypi/$p/json"); echo "$p -> $code"; done; echo "=== pypi search-ish (simple index contains?) ==="; curl -sS -m 12 "https://pypi.org/simple/" | grep -io "reachllm[^/<\"]*" | head; echo "(done)"
=== pypi reachllm ===
=== pypi reachllm ===
404
=== pypi variants ===
reachllm -> 404
reach-llm -> 404
reach_llm -> 404
=== pypi search-ish (simple index contains?) ===
(done)
Thinking…

No package by the name "ReachLLM" exists on npm or PyPI. Let's check a few more things:

  1. Search GitHub for a repository named "ReachLLM."
  2. Check whether there is a website / documentation at a plausible domain.
  3. Check whether there is anything within the git history / repository.

Let's also search more broadly. There might be an official site. Let's try some plausible domains and GitHub.

echo "=== GitHub repo search ==="; curl -sS -m 12 -H "Accept: application/vnd.github+json" "https://api.github.com/search/repositories?q=reachllm&per_page=10" | grep -E '"full_name"|"html_url"|"description"|"total_count"' | head -40; echo "=== GitHub code/org search reachllm ==="; curl -sS -m 12 "https://api.github.com/search/repositories?q=reach+llm&per_page=5" | grep -E '"total_count"' | head
=== GitHub repo search ===
=== GitHub repo search ===
  "total_count": 4,
      "full_name": "codi-ahmed/Reachllmwebsite",
        "html_url": "https://github.com/codi-ahmed",
      "html_url": "https://github.com/codi-ahmed/Reachllmwebsite",
      "description": null,
      "full_name": "maraj0707/ReachLLM",
        "html_url": "https://github.com/maraj0707",
      "html_url": "https://github.com/maraj0707/ReachLLM",
      "description": null,
      "full_name": "maraj0707/ReachLLM-web",
        "html_url": "https://github.com/maraj0707",
      "html_url": "https://github.com/maraj0707/ReachLLM-web",
      "description": null,
      "full_name": "sohazur/ReachMCP",
        "html_url": "https://github.com/sohazur",
      "html_url": "https://github.com/sohazur/ReachMCP",
      "description": "MCP server that turns questions into interactive AI workspaces — powers ReachLLM",
=== GitHub code/org search reachllm ===
  "total_count": 1304,

echo "=== candidate domains ==="; for d in reachllm.com reachllm.ai reachllm.io reachllm.dev getreachllm.com reachllm.co docs.reachllm.com api.reachllm.com; do code=$(curl -sS -m 8 -o /dev/null -w "%{http_code}" "https://$d" 2>/dev/null || echo "ERR"); echo "$d -> $code"; done; echo "(done)"
=== candidate domains ===
=== candidate domains ===
reachllm.com -> 301
reachllm.ai -> 301
reachllm.io -> 000ERR
reachllm.dev -> 000ERR
getreachllm.com -> 000ERR
reachllm.co -> 000ERR
docs.reachllm.com -> 200
api.reachllm.com -> 200
(done)

cd /sandbox/repo && echo "=== git log ==="; git log --oneline -20 2>/dev/null; echo "=== branches ==="; git branch -a 2>/dev/null; echo "=== gitignore ==="; cat .gitignore; echo "=== any reachllm strings in repo/git ==="; grep -riE "reachllm|reach.?llm" . 2>/dev/null | head; git log --all --oneline 2>/dev/null | head
=== git log ===
=== git log ===
9ebf5fb Initial workspace
=== branches ===
* main
=== gitignore ===
# Keep generated and vendored files out of source diffs.

# Logs / OS cruft
*.log
.DS_Store

# JavaScript / TypeScript
node_modules/
.next/
dist/
build/
coverage/

# Python
.venv/
venv/
__pycache__/
*.py[cod]
.pytest_cache/
*.egg-info/

# Rust
target/

# Go / PHP / Ruby vendored deps
vendor/
.bundle/

# Java / Kotlin / Scala
.gradle/

# .NET
bin/
obj/

# Elixir
_build/
deps/

# Dart / Flutter
.dart_tool/
=== any reachllm strings in repo/git ===
9ebf5fb Initial workspace
Thinking…

There are GitHub repositories, and furthermore there are documentation sites (docs.reachllm.com returns 200, api.reachllm.com also returns 200). Let's take a look at the GitHub repositories and documentation sites. There's also "ReachMCP" — "An MCP server that turns questions into interactive AI workspaces — powering ReachLLM."

Let's investigate the following:

  1. The GitHub repositories maraj0707/ReachLLM, maraj0707/ReachLLM-web, sohazur/ReachMCP.
  2. The contents of docs.reachllm.com.
  3. The contents of api.reachllm.com.

This looks like a real (albeit small-scale / new) product. Let's gather the documentation and check whether there's an SDK or API with a developer workflow. The task is as follows: "Build a simple example using ReachLLM. Tell us how the pricing works. Also, briefly tell us whether this product is something you can easily manage. Let us know if you get stuck. If this product doesn't have an actionable developer workflow, be upfront about it and stop."

So the things to do are:

  1. Find the developer workflow (SDK or API).
  2. Build a simple example.
  3. Explain how the pricing works.
  4. Briefly assess the manageability.
  5. Be upfront about it and stop if there's no actionable developer workflow, or if it requires a local service stack or long-running commands.

Let's fetch the documentation and repository READMEs. Let's fetch docs.reachllm.com and the GitHub READMEs.

echo "=== docs.reachllm.com (root) ==="; curl -sSL -m 15 "https://docs.reachllm.com" | head -c 4000; echo; echo "=== api.reachllm.com (root) ==="; curl -sSL -m 15 "https://api.reachllm.com" | head -c 2000; echo "(done)"
=== docs.reachllm.com (root) ===
=== docs.reachllm.com (root) ===
<!DOCTYPE html><html lang="en" dir="ltr" data-theme="dark" data-has-toc data-has-sidebar class="astro-c224ebxq"><head><meta charset="utf-8"/><meta name="viewport" content="width=device-width, initial-scale=1"/><title>Introduction | ReachLLM Docs</title><link rel="canonical" href="https://docs.reachllm.com/"/><link rel="sitemap" href="/sitemap-index.xml"/><link rel="preconnect" href="https://fonts.googleapis.com"/><link rel="preconnect" href="https://fonts.gstatic.com" crossorigin="true"/><link rel="stylesheet" href="https://fonts.googleapis.com/css2?family=Inter:wght@300;400;500;600;700&amp;display=swap"/><link rel="shortcut icon" href="/favicon.ico" type="image/x-icon"/><meta name="generator" content="Astro v7.0.6"/><meta name="generator" content="Starlight v0.41.3"/><meta property="og:title" content="Introduction"/><meta property="og:type" content="article"/><meta property="og:url" content="https://docs.reachllm.com/"/><meta property="og:locale" content="en"/><meta property="og:description" content="Learn what ReachLLM is and how it helps your brand get seen, cited, and recommended by AI platforms."/><meta property="og:site_name" content="ReachLLM Docs"/><meta name="twitter:card" content="summary_large_image"/><meta name="description" content="Learn what ReachLLM is and how it helps your brand get seen, cited, and recommended by AI platforms."/><script>
	window.StarlightThemeProvider = (() => {
		const storedTheme =
			typeof localStorage !== 'undefined' && localStorage.getItem('starlight-theme');
		const theme =
			storedTheme ||
			(window.matchMedia('(prefers-color-scheme: light)').matches ? 'light' : 'dark');
		document.documentElement.dataset.theme = theme === 'light' ? 'light' : 'dark';
		return {
			updatePickers(theme = storedTheme || 'auto') {
				document.querySelectorAll('starlight-theme-select').forEach((picker) => {
					const select = picker.querySelector('select');
					if (select) select.value = theme;
					/** @type {HTMLTemplateElement | null} */
					const tmpl = document.querySelector(`#theme-icons`);
					const newIcon = tmpl && tmpl.content.querySelector('.' + theme);
					if (newIcon) {
						const oldIcon = picker.querySelector('svg.label-icon');
						if (oldIcon) {
							oldIcon.replaceChildren(...newIcon.cloneNode(true).childNodes);
						}
					}
				});
			},
		};
	})();
</script><template id="theme-icons"><svg aria-hidden="true" class="light astro-r7x4y4bx" width="16" height="16" viewBox="0 0 24 24" fill="currentColor" style="--sl-icon-size: 1em;"><path d="M5 12a1 1 0 0 0-1-1H3a1 1 0 0 0 0 2h1a1 1 0 0 0 1-1Zm.64 5-.71.71a1 1 0 0 0 0 1.41 1 1 0 0 0 1.41 0l.71-.71A1 1 0 0 0 5.64 17ZM12 5a1 1 0 0 0 1-1V3a1 1 0 0 0-2 0v1a1 1 0 0 0 1 1Zm5.66 2.34a1 1 0 0 0 .7-.29l.71-.71a1 1 0 1 0-1.41-1.41l-.66.71a1 1 0 0 0 0 1.41 1 1 0 0 0 .66.29Zm-12-.29a1 1 0 0 0 1.41 0 1 1 0 0 0 0-1.41l-.71-.71a1.004 1.004 0 1 0-1.43 1.41l.73.71ZM21 11h-1a1 1 0 0 0 0 2h1a1 1 0 0 0 0-2Zm-2.64 6A1 1 0 0 0 17 18.36l.71.71a1 1 0 0 0 1.41 0 1 1 0 0 0 0-1.41l-.76-.66ZM12 6.5a5.5 5.5 0 1 0 5.5 5.5A5.51 5.51 0 0 0 12 6.5Zm0 9a3.5 3.5 0 1 1 0-7 3.5 3.5 0 0 1 0 7Zm0 3.5a1 1 0 0 0-1 1v1a1 1 0 0 0 2 0v-1a1 1 0 0 0-1-1Z"/></svg><svg aria-hidden="true" class="dark astro-r7x4y4bx" width="16" height="16" viewBox="0 0 24 24" fill="currentColor" style="--sl-icon-size: 1em;"><path d="M21.64 13a1 1 0 0 0-1.05-.14 8.049 8.049 0 0 1-3.37.73 8.15 8.15 0 0 1-8.14-8.1 8.59 8.59 0 0 1 .25-2A1 1 0 0 0 8 2.36a10.14 10.14 0 1 0 14 11.69 1 1 0 0 0-.36-1.05Zm-9.5 6.69A8.14 8.14 0 0 1 7.08 5.22v.27a10.15 10.15 0 0 0 10.14 10.14 9.784 9.784 0 0 0 2.1-.22 8.11 8.11 0 0 1-7.18 4.32v-.04Z"/></svg><svg aria-hidden="true" class="auto astro-r7x4y4bx" width="16" height="16" viewBox="0 0 24 24" fill="currentColor" style="--sl-icon-size: 1em;"><path d="M21 14h-1V7a3 3 0 0 0-3-3H7a3 3 0 0 0-3 3v7H3a1 1 0 0 0-1 1v2a3 3 0 0 0 3 3h14a3 3 0 0 0 3-3v-2a1 1 0 0 0-1-1ZM6 7a1 1 0 0 1 1-1h10a1 1 0 0 1 1 1v7H6V7Zm14 10a1 1 0 0 1-1 1H5a1 1 0 0 1-1-1v-1h16v1Z"/></svg></template><licurl: (23) Failure writing output to destination

=== api.reachllm.com (root) ===
(done)

for r in maraj0707/ReachLLM maraj0707/ReachLLM-web sohazur/ReachMCP codi-ahmed/Reachllmwebsite; do echo "===== $r ====="; for br in main master; do body=$(curl -sSL -m 12 "https://raw.githubusercontent.com/$r/$br/README.md" 2>/dev/null); if [ -n "$body" ] && ! echo "$body" | grep -q "404: Not Found"; then echo "$body" | head -c 2500; echo; break; fi; done; echo; done; echo "(done)"
===== maraj0707/ReachLLM =====
===== maraj0707/ReachLLM =====

===== maraj0707/ReachLLM-web =====
/bin/bash: line 1: warning: command substitution: ignored null byte in input
��# ReachLLM-web


===== sohazur/ReachMCP =====
# Forge

The last MCP app you need. Forge turns any question into an interactive workspace — the AI designs the right tool for your problem on the fly.

No pre-built templates. No rigid forms. You describe what you're thinking through, and Forge generates a custom visual workspace with sliders, comparison tables, scoring cards, pro/con panels, and action buttons — all rendered live inside ChatGPT.

## How it works

1. You ask a question or describe a decision
2. Forge generates an interactive UI spec tailored to your problem
3. You interact — adjust weights, dismiss options, add missing factors
4. Forge synthesizes your inputs into a verdict with next steps

Everything stays in one place. Your inputs persist across the conversation.

## Use cases

- **Decisions** — "Should I stay at my job, join a startup, or go freelance?"
- **Prioritization** — "I have 8 feature requests. Help me rank them by impact and effort."
- **Comparisons** — "Compare three pricing models for my SaaS: per-seat, usage-based, flat-rate."
- **Strategy** — "I'm launching next month. Build me a readiness checklist across marketing, eng, legal, support."
- **Hiring** — "Senior vs junior, full-time vs contractor, local vs remote — help me decide."
- **Investment** — "Compare index funds, rental property, and bootstrapping my startup on risk, return, and effort."

## MCP-to-MCP

Forge can connect to other MCP servers and use their tools. Connect a market sizing MCP, a CRM, or any other deployed MCP server — Forge becomes a single gateway.

## Stack

Built with [mcp-use](https://mcp-use.com). Firebase for persistence. Deployed on Manufact Cloud.

## Run locally

```bash
cd forge
npm install
npm run dev
```

## Deploy

```bash
npm run deploy
```


===== codi-ahmed/Reachllmwebsite =====

(done)
Thinking…

Now I understand what ReachLLM is. Let me organize:

ReachLLM appears to be a product about "getting your brand discovered, cited, and recommended by AI platforms" — in other words, a GEO/AEO (Generative Engine Optimization / Answer Engine Optimization) product. It's about brand visibility in AI answers. The description says "Learn what ReachLLM is and how it helps your brand get discovered, cited, and recommended by AI platforms."

The ReachMCP repository (sohazur/ReachMCP) is actually called "Forge," and it's an MCP server. Its description says it "powers ReachLLM," but the README is about "Forge" — this is a separate/unrelated thing about generating interactive workspaces. It's not an SDK for ReachLLM. This is a red flag — the GitHub repository is either unrelated to the actual product or is personal/abandoned.

The important questions for the task are:

  1. Does ReachLLM have a developer workflow (SDK or API) that I can actually run?
  2. How does pricing work?
  3. Is it easy to manage?

docs.reachllm.com is an actual Starlight documentation site. Let me explore the sitemap to find documentation pages, especially "API" or "SDK" pages, quickstart, and pricing.

The root at api.reachllm.com returned empty (just headers or an empty body). This suggests there might be an API, but there's no obvious documentation at the root.

Let me fetch the sitemap and look for developer-facing pages. Also, let me check whether there's an SDK on npm under a different name.

Let me proceed systematically. The task is essentially a test of whether this product has an actionable developer workflow. I need to verify the following:

  • Is there a published SDK (npm/pypi)? Already confirmed "reachllm" = none.
  • Is there a public REST API with documentation? Need to check docs.reachllm.com/sitemap.
  • Is there an API key/authentication mechanism?

Let me fetch the sitemap and pricing page.

The curl to docs.reachllm.com failed with "Failure writing output to destination" because of head -c on a large file, but I was able to get the intro. Let me fetch the sitemap-index and the sitemap, then fetch specific pages. To avoid write failures, let me save to a file and grep.

cd /tmp && curl -sSL -m 15 "https://docs.reachllm.com/sitemap-0.xml" -o sitemap.xml 2>/dev/null; echo "=== URLs in sitemap ==="; grep -oE "<loc>[^<]+</loc>" sitemap.xml | sed -E 's/<\/?loc>//g'; echo "=== count ==="; grep -c "<loc>" sitemap.xml
=== URLs in sitemap ===
=== URLs in sitemap ===
https://docs.reachllm.com/
https://docs.reachllm.com/advanced/reachllm-agent/
https://docs.reachllm.com/advanced/support/
https://docs.reachllm.com/advanced/teams-and-white-label/
https://docs.reachllm.com/getting-started/dashboard/
https://docs.reachllm.com/getting-started/plans-and-pricing/
https://docs.reachllm.com/getting-started/quickstart/
https://docs.reachllm.com/guides/ai-visibility-tracking/
https://docs.reachllm.com/guides/bot-view/
https://docs.reachllm.com/guides/content-generation/
https://docs.reachllm.com/guides/creating-your-website/
https://docs.reachllm.com/guides/geo-audit/
https://docs.reachllm.com/guides/pr-outreach/
https://docs.reachllm.com/guides/troubleshooting/
https://docs.reachllm.com/guides/understanding-the-scores/
https://docs.reachllm.com/guides/working-with-prompts/
https://docs.reachllm.com/integrations/bing-webmaster-tools/
https://docs.reachllm.com/integrations/github/
https://docs.reachllm.com/integrations/google-analytics-4/
https://docs.reachllm.com/integrations/google-search-console/
https://docs.reachllm.com/integrations/mcp/
https://docs.reachllm.com/integrations/overview/
https://docs.reachllm.com/integrations/reachllm-llm-analytics/
https://docs.reachllm.com/integrations/shopify/
https://docs.reachllm.com/integrations/webflow/
https://docs.reachllm.com/integrations/wordpress-plugin/
=== count ===
1

cd /tmp && echo "=== api.reachllm.com verbose ==="; curl -sSL -m 12 -D - "https://api.reachllm.com" -o apiroot.txt; echo "--- body ---"; cat apiroot.txt | head -c 1500; echo; echo "=== common api paths ==="; for p in /health /healthz /v1 /api /docs /openapi.json /openapi.yaml /swagger.json /status /version /pricing; do code=$(curl -sSL -m 8 -o /dev/null -w "%{http_code}" "https://api.reachllm.com$p" 2>/dev/null || echo ERR); echo "$p -> $code"; done
=== api.reachllm.com verbose ===
=== api.reachllm.com verbose ===
HTTP/2 200 
alt-svc: h3=":443"; ma=2592000
server: Caddy
content-length: 0
date: Sun, 01 Nov 2026 16:20:44 GMT

--- body ---

=== common api paths ===
/health -> 200
/healthz -> 200
/v1 -> 200
/api -> 200
/docs -> 200
/openapi.json -> 200
/openapi.yaml -> 200
/swagger.json -> 200
/status -> 200
/version -> 200
/pricing -> 200
Thinking…

Interesting. So:

  • docs.reachllm.com is the actual Starlight documentation site, and the following pages exist: quickstart, plans-and-pricing, dashboard, integrations (mcp, github, GA4, GSC, Shopify, Webflow, WordPress), guides, and advanced (reachllm-agent, teams-and-white-label, support).

  • api.reachllm.com returns 200 with content-length: 0 for every path (/, /health, /v1, /docs, /openapi.json, /pricing, etc.). This is a classic catch-all that returns an empty 200 — it's a Caddy server returning an empty body for everything. In other words, api.reachllm.com is not an actually documented API; it returns an empty 200 for everything. That's a red flag: there is no real, documented REST API. The fact that /openapi.json returns a 200 with an empty body (content-length 0) means it's not an actual OpenAPI specification.

Let's confirm that the API returns an empty body for everything (catch-all). content-length: 0 for all paths strongly suggests it's a catch-all with no real endpoints.

Now, the important pages for the task are:

  • getting-started/quickstart — is there a developer workflow?
  • getting-started/plans-and-pricing — pricing.
  • integrations/mcp — there's an MCP integration (this connects to the ReachMCP/Forge repo).
  • advanced/reachllm-agent — "ReachLLM Agent."

This product is essentially a marketing/SaaS product: "Get your brand discovered, cited, and recommended by AI platforms" — GEO/AEO. The workflow is probably dashboard-driven (a hosted web app), and it may not have a programmatic SDK/API for developers. Integrations include MCP, WordPress plugin, Shopify, Webflow, GitHub, GA4, GSC.

The task says: "Use the hosted product through its SDK or API. Do not spin up a local service stack… If the quickstart requires either, say so clearly and stop." And "If there is no actionable developer workflow for this product, say so clearly and stop."

So I need to read the quickstart and determine whether there's an actionable developer workflow (SDK or API). Let me fetch the quickstart, plans-and-pricing, integrations/overview, integrations/mcp, and advanced/reachllm-agent. Let me extract the text content.

The docs are Astro/Starlight HTML. Let me fetch them and strip the HTML. Let me write a little extraction. I'll use curl + text processing (python or sed to remove tags).

Let me fetch the key pages.

cd /tmp && cat > strip.py <<'PY'
import sys,re,html
t=sys.stdin.read()
# keep only main content if possible
m=re.search(r'<main[^>]*>(.*?)</main>', t, re.S)
body=m.group(1) if m else t
# drop scripts/styles/svg/template
body=re.sub(r'<(script|style|svg|template|noscript)[^>]*>.*?</\1>','',body,flags=re.S)
# add newlines for block tags
body=re.sub(r'<(h[1-6]|p|li|pre|/tr|/div|br)[^>]*>','\n',body)
body=re.sub(r'<[^>]+>','',body)
body=html.unescape(body)
lines=[l.rstrip() for l in body.splitlines()]
out=[]
for l in lines:
    if l.strip()=='' and (not out or out[-1]==''):
        continue
    out.append(l.strip())
print('\n'.join(out).strip())
PY
echo "helper ready"
helper ready
helper ready

cd /tmp && for p in getting-started/quickstart getting-started/plans-and-pricing; do echo "############ $p ############"; curl -sSL -m 15 "https://docs.reachllm.com/$p/" | python3 strip.py; echo; done
############ getting-started/quickstart ############
############ getting-started/quickstart ############
Quickstart

This guide takes you from a new account to your first AI visibility results. You only need your website domain to get started.

1. Create your accountSection titled “1. Create your account”

Sign up at app.reachllm.com with your company email. This requirement applies to email and password signup and to Continue with Google. Existing accounts can continue to sign in with their current method.

After you review your brand, competitors, and tracked prompts, ReachLLM sends you to plan selection. If you return before finishing Checkout, sign-in takes you back to plan selection before the walkthrough. Complete Stripe Checkout to start your 3 day trial before the first metered analysis or website build begins. Enterprise Agencies starts immediately without a trial. Stripe collects a payment method during Checkout and automatically bills eligible trial plans when the trial ends unless you cancel first. You get one trial per account ever. If you have already used a trial on any plan or brand, a new subscription starts billing immediately. After a trial ends, you can still review the workspace, but actions that create new work prompt you to choose a paid plan. See Plans and pricing for what each tier includes.

If you start from the free report flow, ReachLLM asks for your website, brand name, alternate names, and at least three competitors. Progress is saved quietly while you complete the form, as disclosed beside the first step, so an interrupted request can be recovered. The final contact details are saved before submission. If a mobile connection drops before confirmation returns, ReachLLM safely checks the same saved request again instead of creating a duplicate. Confirmation email delivery runs separately, so a slow email provider cannot hold the form open or turn an accepted request into a visible failure. The competitor suggestions are discovered from your website, and you can add your own domains. Alternate names help ReachLLM match brand mentions correctly in the report. ReachLLM researches 50 personalized buyer prompts for the brand and runs all 50 across the supported AI platforms. The report is published only after every prompt has a successful result from ChatGPT through OpenAI, Google AI Overview through SerpAPI, Perplexity through Perplexity, and Gemini through Google. An aggregator response is never substituted for one of these measurements. If one provider has a temporary gap, ReachLLM automatically retries the same 50 prompts in the background. A failed recovery run advances to the next scheduled attempt instead of leaving the report unfinished. The same 50 prompts and completed analysis are saved to your ReachLLM account as soon as the verified report is published, even if the delivery email is delayed. Free reports keep every competitor you selected and show whether its website and buyer need were verified, whether it was measured, and whether it appeared in the saved answers. ReachLLM researches the closest relevant alternatives from official websites and uses their actual appearances in those answers to fill comparison gaps. A verified competitor with no mentions is shown separately from one that was not verified or measured. If fewer than three verified competitors appear, the report still shows your competitor list and available results, while comparative percentages remain unavailable. When the private report account is ready, request a password setup link from the sign-in screen and use it to open the saved prompts, responses, and report history. You can request a fresh link if an earlier one has expired. If you sign in while the report is still running, ReachLLM shows its preparation status instead of asking you to repeat brand setup. When it is ready, the account opens the saved Brand Visibility results directly. If an account-access check fails, ReachLLM stops the loading indicator and offers Try again and a support link. An access check times out after 20 seconds instead of leaving the screen loading indefinitely. If a report is already published but its saved workspace cannot be opened, ReachLLM shows the same retry and support options instead of saying the report is still being prepared. Shared report links also offer Try again when a connection fails; a temporary connection problem is shown separately from a report that is no longer published. Starting new work still follows the plan and usage limits shown in the app.

Free report questions are checked against official evidence from the website you submitted before analysis begins. Use alternate names for real brand spellings or abbreviations, not generic services or industry terms. If ReachLLM cannot verify the business or the relevance of all 50 questions, it holds the report for investigation rather than sending an unchecked result. See Free report accuracy checks.

2. Add your brandSection titled “2. Add your brand”

Enter your website domain. You can also type a city, region, or country in the optional location field. ReachLLM suggests matching places with country flags, so you can select the correct market before continuing. ReachLLM analyzes your site, discovers your competitors, and researches exactly 50 tracked prompts your brand should be visible for. Tracked prompts are the questions users ask AI platforms that you want your brand to appear in. ReachLLM saves the complete prompt set before Stripe Checkout. The same exact-50 setup and recovery applies when an eligible workspace adds another brand. A new brand does not become the selected workspace until its complete prompt inventory is saved. If setup is interrupted or a research provider is temporarily unavailable, your reviewed brand details stay saved and returning to setup retries prompt research only. You do not need to repeat the four brand-review steps.

3. Review prompts and competitorsSection titled “3. Review prompts and competitors”

Before running an analysis, take a moment to check what was generated:

Prompts: finalize the prompt list before the first visibility run. Review prompts grouped by topic, or switch to the raw prompt list to scan every question one by one. Edit, delete, or add prompts here so the first analysis measures the questions you actually care about. See Working with prompts.

Competitors: competitors are auto-discovered during brand analysis and fully editable. Classify them as direct, indirect, or SERP competitors, keep other companies in review, add or remove entries, and set aliases for alternate spellings so mentions are matched correctly.

Tip
A quick review here pays off. Accurate prompts and competitors make every score you see later more meaningful.

4. Run your first visibility analysisSection titled “4. Run your first visibility analysis”

After Stripe Checkout confirms an active subscription or trial, ReachLLM automatically starts the first Brand Visibility analysis with the complete reviewed prompt set and a GEO audit of your homepage. If you close Checkout after completing it or sign in again without its return page, opening the workspace resumes the saved first analysis after active access is confirmed. This also works while you watch the introduction or complete the Get Started checklist. Returning to the checkout confirmation or reopening the workspace reuses the same initial run and homepage audit. Expired trial access lets you review saved results and does not start new work. If your workspace already has active access, setup continues without creating another trial.

Once the visibility analysis and GEO audit are complete, ReachLLM also prepares your strategy plan automatically. When the visibility run finishes, open the Overview tab to see your key metrics:

Visibility Score: the percentage of your tracked prompts where a platform's answer mentions your brand.

Share of Voice: of all brand appearances in the analyzed answers, the percentage that are yours.

Average Rank: when your brand appears, the average position at which it is mentioned relative to other brands. Lower is better.

For a full explanation of every metric, read Understanding the scores.

5. Open your GEO auditSection titled “5. Open your GEO audit”

The GEO audit checks your site page by page: titles, meta descriptions, headings, structured data, llms.txt, page speed, and more, plus off-page authority and social presence. Each issue comes with a severity level (Critical, High, Medium, or Low), an explanation, and a recommended fix. Start with the Critical issues. See the GEO Audit guide.

6. Ask the agent for a summarySection titled “6. Ask the agent for a summary”

Open the Agent page (or the floating sidebar available on every page) and ask something like "Summarize my visibility results and top GEO issues." The agent can analyze your visibility, run audits, generate content, and much more. See The ReachLLM agent.

Keep going with the checklistSection titled “Keep going with the checklist”

Your Dashboard includes a Get Started checklist that walks you through the remaining setup: building your knowledge base, connecting Google Search Console and Analytics, and inviting your team.

Next stepsSection titled “Next steps”

Dashboard and onboarding

AI visibility tracking

Plans and pricing

Previous
IntroductionNext
Plans and Pricing

############ getting-started/plans-and-pricing ############
Plans and Pricing

ReachLLM offers self-serve plans for brands and agencies, a managed service tier (Growth), and custom Enterprise agreements. Every plan includes AI visibility tracking across ChatGPT, Google AI Overviews, Perplexity, and Gemini. Use the Brands and Agencies switch on the pricing page or in Billing to compare the relevant third plan.

Self-serve plansSection titled “Self-serve plans”

Pro fits a single brand. Scale fits agencies and multi-brand teams with up to 10 projects. Enterprise Agencies fits larger client portfolios with unlimited projects. Scale and Enterprise Agencies pool their allowances across all projects in the workspace.

If ReachLLM grants a custom project, tracked-prompt, or execution-credit allowance, the Usage this period meters in Billing show the account's effective limits. Pooled prompt usage includes every active project in Scale and Enterprise Agencies workspaces. The plan comparison continues to show the standard limits included with the base plan.

Pro
Scale
Enterprise Agencies

Price (monthly)
$399/month
$999/month
$1,599/month

Price (yearly)
$3,990/year
$9,990/year
$15,990/year

Brand projects
1
Up to 10 (limits pooled)
Unlimited (limits pooled)

Tracked prompts per month
100
500
1,000

GEO audit pages per month
100
1,000
2,500

AI articles per month
10
50
50

Execution credits per month
4,000
20,000
40,000

AI models included
ChatGPT, Google AI Overviews, Perplexity, Gemini
ChatGPT, Google AI Overviews, Perplexity, Gemini
ChatGPT, Google AI Overviews, Perplexity, Gemini

White-label reports
No
Yes
Yes

Traffic integrations
No
Yes
Yes

GrowthSection titled “Growth”

Growth is a managed service tier for one managed brand project: the ReachLLM team runs execution for you. Pricing starts at $3,500/month ($35,000 yearly) with a 3 month minimum commitment. Growth includes Scale-level prompt, model, audit, and execution limits for that project, plus white-label reports and fully managed PR outreach. Additional managed projects require a separately scoped agreement. To get started, contact sales or book a strategy call.

EnterpriseSection titled “Enterprise”

Enterprise plans come with custom pricing and custom limits tailored to your organization. Contact team@reachllm.com to discuss your requirements.

Execution creditsSection titled “Execution credits”

Execution credits pay for the token-heavy AI work on the platform: the ReachLLM agent (chat), website generation, and manual Brand Visibility refreshes. Every AI run reports its real model cost, and that cost is converted to execution credits at a fixed rate ($1 of AI cost equals 100 credits).

What does and does not spend execution credits:

Spends credits: ReachLLM agent chat, website generation and redesign, and manual Brand Visibility Refresh Now runs. Manual visibility refreshes show the credit estimate before they start.

Does NOT spend credits: GEO audits (metered by unique pages per month) and scheduled Brand Visibility tracking (metered by how many prompts you track).

Your monthly execution-credit usage counter resets at the same time each month, based on the subscription start date. This monthly reset also applies when the subscription is paid yearly. Stripe keeps the separate monthly or yearly renewal date. Purchased top-up credits are not reset. If the monthly allowance and top-up balance are exhausted, metered agent chat, website generation, and manual visibility refreshes stop before the next run and show a prompt to buy credits or upgrade.

Buying more credits (top-ups)Section titled “Buying more credits (top-ups)”

You can buy extra execution credits any time from the Billing tab. After Stripe confirms the payment, the credits are added automatically and appear in your Billing balance. Top-up credits never expire and roll over month to month, and they are only spent after your monthly allowance is used up.

Top-up pack
Price

4,000 credits
$79

8,000 credits
$149

20,000 credits
$299

100,000 credits
$999

ReachLLM platform admin workspaces have unlimited usage allowances. Authorized team members use the workspace owner's allowance for tracked prompts, GEO audit pages, and execution credits. This does not grant team members platform administrator access or access to additional projects. Usage continues to be recorded even when an allowance is unlimited. Customer plan limits continue to apply normally to Pro, Scale, Enterprise Agencies, Growth, and Enterprise accounts.

AI model add-onsSection titled “AI model add-ons”

Pro, Scale, and Enterprise Agencies include ChatGPT, Google AI Overviews, Perplexity, and Gemini. You can add extra AI platforms to your tracking as monthly add-ons:

Add-on
Pro price (per month)
Scale price (per month)
Enterprise Agencies price (per month)

Google AI Mode
$50
$250
$500

Microsoft Copilot
$50
$250
$500

Claude
$200
$1,000
$2,000

The plan comparison and Billing tab show the provider logo for each add-on, including Microsoft Copilot.

Free trialSection titled “Free trial”

New accounts selecting Pro or Scale get a 3 day free trial. Trials granted before this change keep their original end date. Each account gets one trial ever, across all plans and brands. Enterprise Agencies subscriptions start billing immediately. New self-serve accounts select a plan after finalizing their first brand and tracked prompts. Stripe Checkout requires a payment method before the trial starts. Stripe automatically bills the selected plan when the trial ends unless you cancel before the trial end date.

Access is granted only after ReachLLM verifies the completed Stripe Checkout session and confirms that the subscription is active or trialing. Returning to the app with a modified payment URL does not activate a plan.

Note
After you have used your trial once, any new subscription starts billing immediately, with no second trial period.
Some invited accounts may receive complimentary Pro access without entering payment details. Billing shows the Pro plan and the complimentary end date, but no Stripe subscription is created and no automatic charge can occur. Complimentary access counts as the account's one free access period. Continuing on Pro, Scale, or another paid plan requires Stripe Checkout and starts paid billing immediately, with no additional free trial.

An active selected-plan trial or complimentary access period receives the full limits of that plan. Pro access includes 100 tracked prompts and 100 unique GEO audit pages per month. Billing shows both the current usage and the plan allowance. When an allowance is reached, ReachLLM stops the next new prompt or GEO page action and opens Billing so the workspace owner can upgrade or subscribe.

When a trial ends without an active subscription, you can still sign in and review the workspace. Actions such as running analysis, adding projects, creating content, or using metered agent workflows prompt you to choose a paid plan before continuing. If Stripe cannot collect the first invoice, the account does not receive active paid access.

If you cancel during an active trial, access stops immediately. You can resume the remaining trial window from the Plans page until the original trial end date. Resuming does not create a second trial.

Managing your subscriptionSection titled “Managing your subscription”

You manage subscription changes from the Billing tab in your account settings. Trial accounts see Cancel trial in Billing management. Accounts with a direct Stripe plan see Cancel plan or Resume plan in Billing management. Per-brand subscriptions also show cancellation controls in Subscription records.

Some grandfathered agency workspaces are billed through Paddle. Billing identifies these as Paddle managed, shows the workspace's current plan and limits, and does not send the owner to Stripe. Contact ReachLLM support for invoices, payment methods, or plan changes on a Paddle-managed workspace.

Upgrade: move from Pro to Scale or Enterprise Agencies at any time.

Downgrade: switch to a lower plan from the same tab.

Cancel: start the cancellation flow from Billing. ReachLLM shows what access is affected, asks for a short reason, and confirms the timing before anything changes.

Paid subscriptions stay active until the end of the current billing period after cancellation. You can resume the subscription from Billing before that date. Trial cancellations take effect immediately, but you can still resume the remaining trial window from the Plans page if the original trial period has not ended.

You can also book a strategy call from the Billing tab if you want help choosing a plan.

Next stepsSection titled “Next steps”

Set up your first brand with the Quickstart.

Learn what the platform tracks in AI visibility tracking.

See how audited pages count toward your limits in the GEO audit guide.

Previous
QuickstartNext
Understanding Your Dashboard
Thinking…

Now I have a very clear overall picture. ReachLLM is a SaaS product for GEO/AEO (Generative Engine Optimization). The workflow is entirely dashboard-driven:

  1. Create an account at app.reachllm.com
  2. Add a brand (website domain)
  3. Review prompts and competitors
  4. Run visibility analysis (after Stripe checkout/trial)
  5. Open a GEO audit
  6. Query the agent

Pricing is clear: Pro $399/month, Scale $999/month, Enterprise Agencies $1,599/month, Growth (managed) starting at $3,500/month, Enterprise is custom. Plus execution credits, top-ups, add-ons, free trial (3 days).

Now, the important question is: does ReachLLM have a developer workflow that I can actually run (SDK or API)?

The quickstart is entirely about the web dashboard at app.reachllm.com — sign up, Stripe checkout, add a brand, run analysis. There is no SDK, no API keys, no REST API endpoints documented in the quickstart. The "Agent" is an in-app chat agent, not a programmatic API.

Regarding integrations, there's an MCP integration page and a "ReachLLM LLM Analytics" integration. Let's check integrations/mcp and integrations/overview to see if there's a programmatic API. The MCP integration might be the closest thing to a "developer workflow." However, MCP is about connecting ReachLLM to ChatGPT/Claude via MCP, and probably requires a hosted MCP server and authentication — and the "ReachMCP/Forge" repo on GitHub is actually a different product ("Forge") that just says it "powers ReachLLM" — it's not ReachLLM's own SDK.

Also, api.reachllm.com returns an empty 200 for everything — it's a catch-all with no actual endpoints and no OpenAPI spec. So there's no documented public REST API.

Let's check the integrations/overview, integrations/mcp, advanced/reachllm-agent, and integrations/reachllm-llm-analytics pages to be thorough about whether there's an actionable developer workflow (SDK/API). Additionally, let's confirm that api.reachllm.com is a catch-all (empty for all methods/paths).

Task instructions:

  • "Use the hosted product through its SDK or API."
  • "If there is no actionable developer workflow for this product, say so frankly and stop."
  • "If the quickstart requires either [a local service stack or a long-running command], say so frankly and stop."

So I need to determine: is there an SDK or API I can use? Let's check the integration pages for any mention of an API/SDK. Let's fetch those pages.

Let's do this thoroughly before drawing conclusions. Let's fetch integrations/overview, integrations/mcp, integrations/reachllm-llm-analytics, advanced/reachllm-agent, and also search the entire docs for "API" / "SDK" / "endpoint" / "Bearer" / "api key".

cd /tmp && for p in integrations/overview integrations/mcp integrations/reachllm-llm-analytics advanced/reachllm-agent; do echo "############ $p ############"; curl -sSL -m 15 "https://docs.reachllm.com/$p/" | python3 strip.py | head -c 4500; echo; echo; done
############ integrations/overview ############
############ integrations/overview ############
Integrations Overview

ReachLLM integrations are grouped by what they let the platform do for a brand.

Search dataSection titled “Search data”

Search data integrations help ReachLLM connect AI visibility work to real search behavior:

Google Search Console

Google Analytics 4

Bing Webmaster Tools

LLM Analytics

Search integrations feed Traffic, GEO Audit context, and agent analysis. LLM Analytics appears in Traffic with its own per-brand server-request connection and live status.

Content & CMSSection titled “Content & CMS”

Content & CMS integrations are the first-class publishing paths. They connect to the cmsConnection adapter system, so the app and agent can use one shared contract for status, capabilities, content retrieval, media upload, article drafts, page updates, metadata, schema, llms.txt, and robots.txt.

Active publishing paths:

WordPress: publish article drafts, upload media into the Media Library, update pages and metadata, push schema, llms.txt, and robots.txt.

GitHub: inspect a connected repository, read files, commit image assets, and open pull requests or direct commits for content and technical fixes.

Webflow: list and read static pages, upload image assets, publish CMS items, update CMS item content, update metadata, and publish article drafts to CMS collections.

Shopify: list and read Online Store pages and blog articles, upload theme image assets, publish article drafts, update page/article content, update metadata, and create llms.txt pages.

Website Generator source extractionSection titled “Website Generator source extraction”

When a Content & CMS integration is connected, Website Generator and page-source tools check the connected CMS first, then fall back to sitemap discovery. This lets ReachLLM extract the current source page before generating, redesigning, or fixing content.

Supported CMS source reads include:

WordPress pages and posts.

GitHub source files from the connected repository.

Webflow static pages and CMS items.

Shopify Online Store pages, blog articles, and theme assets when theme permissions are available.

The page source response marks whether the source came from the CMS or from the generated-site fallback, so the agent can choose the correct update path.

Workspace appsSection titled “Workspace apps”

Workspace apps give the agent context or action access outside the publishing adapter system. Examples include Google Docs, Google Drive, Google Sheets, Notion, Figma, Gmail, Outlook, Slack, Reddit, LinkedIn, calendars, and meeting tools.

LinkedIn supports Strategy feed posts through the connected LinkedIn account. In Account > Integrations, choose the posting account before publishing: a personal profile or a company page. If the company page is not discovered automatically, paste the LinkedIn company page URL or the numeric ID from the URL, for example 11756149 from /company/11756149/. Company pages require the connected LinkedIn member to have page posting access. LinkedIn Pulse style article work is draft and link-share only for now: ReachLLM can draft the long-form article and share an existing article URL, but the current LinkedIn connection does not expose native long-form article publishing.

External AI clientsSection titled “External AI clients”

MCP Connector connects external AI clients such as Claude, Claude Code, Cursor, Codex, Windsurf, and Cline to ReachLLM through a remote MCP server. Generate an MCP access token in Account > MCP, choose account or brand scope, then add the ReachLLM server URL to your client.

Choosing the right pathSection titled “Choosing the right path”

Use Content & CMS when the goal is to publish or edit the website. Use workspace apps when the agent needs context, outreach, collaboration, or file access. Use MCP when you want an external AI client to work with ReachLLM context and actions.

Previous
Troubleshooting and FAQNext
Google Search Console


############ integrations/mcp ############
MCP Connector

The ReachLLM MCP connector lets Claude Code, Cursor, Codex, Windsurf, Cline, and other compatible Model Context Protocol clients work with your ReachLLM account.

Use it when you want an external AI assistant to use ReachLLM context and actions from your editor or chat client. The external client supplies the chat and model compute. ReachLLM supplies authenticated data, tools, plan checks, connected integrations, and backend processing.

What MCP can doSection titled “What MCP can do”

Depending on your account, brand access, and connected integrations, MCP tools can:

List the brands available to your account.

Read Brand Visibility results, exact source inventories, sentiment, competitors, and detailed responses.

Check and update a brand's official display name and own-brand alternate names used for mention matching.

List and edit competitor knowledge, including direct, indirect, SERP, and potential classifications plus aliases.

Run Brand Visibility checks when your plan allows it.

Run GEO audits and inspect technical, content, authority, and social issues.

Read saved Bot View previews, inspect a public page, and compare the initial response with a clean browser.

Read connected LLM Analytics crawler request counts, pages, statuses, and recent metadata.

Generate prompts, content, llms.txt, and strategy recommendations.

Read Content Studio drafts, versions and review records without editing them.

Use connected publishing paths such as WordPress, GitHub, Webflow, or Shopify.

Read connected search data such as Google Search Console, Google Analytics 4, and Bing Webmaster Tools.

MCP uses the same ReachLLM tool layer the in-app ReachLLM agent uses where the tool is supported. That includes reading context, running analysis, creating content, editing configured connected surfaces, and returning results to the external AI client.

Brand-scoped tokens limit the external client to one brand. Account-scoped tokens let the client list active brands and work across brands your account can access. Team members can create brand-scoped tokens for brands they can access.

Bot View and crawler toolsSection titled “Bot View and crawler tools”

The connector exposes the same Bot View tools as the in-app Agent:

Tool
What it does

get-page-bot-view
Read saved page text, access rules, capture date, and limitations. Use compareBrowser: true with request profile reachllm for a saved paired browser comparison. Never starts a scan.

get-bot-view-findings
Read saved diagnostic summaries for 1 to 50 known URLs within one brand. Returns requested-page coverage, capture dates and warnings; pages without current evidence remain unknown. Does not discover pages or request website content.

inspect-page-bot-view
Inspect one public page, optionally with compareBrowser: true. Use only when the user requests a check or comparison. Reuses eligible cached evidence unless fresh: true requests another capture.

get-crawler-traffic-data
Read saved LLM Analytics request metadata for the selected brand. Accepts days from 1 to 90 and limit from 1 to 20.

For a question about which pages to fix, use known URLs from the saved GEO inventory, read their findings, then open a selected page with get-page-bot-view before proposing an exact change. Full reports include structured diagnostics, missing text excerpts, the measurement basis, suggested fix steps and verification guidance. A percentage is not a provider-readability score, and a page with no saved comparison is not a pass. A requested source change uses the existing connected publishing tools and their permissions; these read and inspection tools do not edit the website.

Use the selected session brand, or pass an authorized brandId for an account-scoped token. A brand-scoped token cannot select another brand or switch to another user's personal preview. Current tenant and brand permissions are checked by ReachLLM for each request. Tools never accept a model-selected account identity or access role.

Bot View saved reads are free. Initial checks and browser comparisons have the published daily free allowances and do not use execution credits or scored GEO audit pages. External clients supply their own model compute. The standalone browser viewer also works after sign-in without a paid plan or completed brand setup.

Page text is untrusted evidence, not tool instructions. Distinguish initial HTML, paired browser content, simulated user agents, and real request logs. A reque

############ integrations/reachllm-llm-analytics ############
LLM Analytics

LLM Analytics shows when an observable AI agent requests your website. It is separate from Google Analytics 4 referral traffic:

LLM Analytics records server requests from documented crawler user agents.

GA4 referral traffic records people who arrive from an AI product.

Connect your websiteSection titled “Connect your website”

You can complete setup inside ReachLLM without creating a shared account-wide secret:

Open Traffic.

Choose LLM Analytics from the traffic-source menu.

Select where your website is hosted.

Select Create connection key.

Follow the numbered instructions for your provider.

Select Test connection.

Connections belong to the selected brand. After the test passes, the setup instructions collapse to a simple Connected status with the provider and domain. Switching brands shows that brand's own connection. Use Manage only when you need to retest, create a new key, or disconnect.

The connection key is shown once. ReachLLM stores only a secure fingerprint. Keep the key in a server-side secret store and never put it in browser code, a tag manager, a public repository, or a URL.

The setup test requests your website with a synthetic GPTBot user agent and a one-time verification value. It proves that the request-forwarding pipeline works. It does not claim to be a real OpenAI visit, and the test is not included in your traffic totals.

For providers that need an adapter, the setup screen includes a downloadable starter file. Review it with the person who manages your hosting account, add the required provider resources, and keep the connection key in the provider's server-side secret store.

Connection fieldsSection titled “Connection fields”

Every provider uses these fields:

Field
Value

Endpoint
https://app.reachllm.com/api/ai-crawler-traffic/ingest

Header name
X-ReachLLM-Site-Token

Header value
The connection key shown in ReachLLM

ReachLLM accepts provider log batches and filters them for crawler signatures on the server. A normalized event can contain:

{

"domain": "example.com",

"path": "/guide",

"user_agent": "GPTBot/1.0",

"timestamp": "2026-07-15T10:30:00Z",

"method": "GET",

"status_code": 200,

"referer": "",

"country": "AE",

"response_time_ms": 142,

"request_id": "provider-request-id"

}

You can also send { "events": [...] }. ReachLLM strips query strings, derives crawler identity from the raw user agent, rejects domains outside the selected brand, and deduplicates retries by request ID.

CloudflareSection titled “Cloudflare”

Use this option when website traffic passes through Cloudflare and the relevant DNS record is Proxied.

Open Workers & Pages and create a Worker. If another Worker already owns the route, add this handler to that Worker.

Store the ReachLLM connection key as the secret REACHLLM_SITE_TOKEN.

Add the route yourdomain.com/*.

Deploy the Worker.

Return to ReachLLM and select Test connection.

This minimal handler returns the origin response immediately and sends crawler analytics with waitUntil, so visitors do not wait for analytics:

const AI_CRAWLER = /GPTBot|OAI-SearchBot|ChatGPT-User|OAI-AdsBot|ClaudeBot|Claude-SearchBot|Claude-User|anthropic-ai|Claude-Web|PerplexityBot|Perplexity-User|Google-CloudVertexBot|Google-Agent|Google-NotebookLM|Applebot|meta-externalagent|meta-externalfetcher|Amazonbot|Amzn-SearchBot|Amzn-User|MistralAI-Index|MistralAI-User|YouBot|DuckAssistBot|CCBot|AI2Bot|DeepSeekBot|Grok-DeepSearch|xAI-Web-Crawler|xAI-SearchBot|Bytespider|cohere-ai/i;

export default {

async fetch(request, env, context) {

const startedAt = Date.now();

const response = await fetch(request);

const url = new URL(request.url);

const userAgent = request.headers.get("user-agent") || "";

const verificationNonce = url.searchParams.get("reachllm_verify") || "";

if (AI_CRAWLER.test(userAgent) || verificationNonce) {

context.waitUntil(fetch("https://app.reachllm.com/api/ai-crawler-traffic/ingest", {

method: "POST",

headers: {

"Content-Type": "application/json",

"X-ReachLLM-Site-Token": env.REACHLLM_SITE_TOKEN,

},

body: JSON.stringify({

domain: url.hostname,

path: url.pathname,

user_agent: userAgent,

timestamp: Date.now(),

method: request.method,

status_code: response.status,

response_time_ms: Date.now() - startedAt,

country: request.cf?.country || "",

request_id: request.headers.get("cf-ray") || crypto.randomUUID(),

verification_nonce: verificationNonce,

}),

}));

}

return response;

},

};

Cloudflare documents 

############ advanced/reachllm-agent ############
The ReachLLM Agent

The ReachLLM agent is the built-in assistant for brand visibility work. It behaves less like a chatbox and more like an AEO copilot: it plans multi-step work, searches the live web with sources, runs tasks in parallel when it can, and remembers reusable workflows as skills.

The Agent checks the workspace's current execution-credit balance before each response. Monthly credits and non-expiring top-up credits are combined. If you add credits or upgrade after a response was stopped, the old response remains in the conversation as history, but the warning clears as soon as the live balance can cover that response.

Authorized team members use the billing owner's execution-credit allowance. If the owner has unlimited usage, Agent responses keep recording usage without applying a credit cap. Team members still only have access to their assigned projects and existing permissions.

A teammate must connect as themselves on each computer. Installing an external client does not grant a role or copy another user's connection. Shared brand data stays subject to current permissions; private chat history is separate.

What it can doSection titled “What it can do”

The agent can:

Explain visibility, traffic, and GEO results.

Answer questions about your own account: which plan you are on, how many execution credits you have used and have left (including non-expiring top-up credits), your tracked prompt, GEO audit page, and project limits, and which AI models are tracked for you. These come from your live billing record, so they match what Account > Billing shows.

Read connected AI crawler traffic, including crawler totals, top bots, top pages, daily trends, and recent visits for the active brand.

Explain saved Bot View page text, access rules by purpose, and browser differences, or run a bounded free inspection when you ask.

Compare two visibility runs and tell you exactly which prompts gained or lost a mention for a specific AI platform (for example, "which prompts dropped for Perplexity between June 24 and the latest run"), and separate a real lost mention from a platform that simply was not queried that run.

Turn Brand Visibility answers into inline charts for visibility score, share of voice, average rank, mentions, citation rate, trends, and competitor comparisons. Use the metric buttons to change the chart and View data to inspect the values in a table.

Handle a rebrand. Ask it to change your brand name, website domain, or alternate names and it updates the existing brand in place, so you do not end up with a duplicate. Keep the old name in your aliases: AI platforms trained before the rebrand will still use it, and the aliases are what keep those mentions credited to you. GEO audits and visibility runs from before the change still refer to the old domain, so the next run is your first clean baseline on the new one.

Run or inspect audits. For new page audits, the Agent reports the deterministic core issue score, passed, failed, and not-counted totals separately from supplementary GEO checks. It keeps historical overall health clearly labeled and does not treat a not-counted check as an issue.

Platform admins can update Brand Visibility model preferences in Account settings or ask the Agent to switch models directly. ChatGPT choices use GPT-5.6 Sol Medium, High, Extra High, or Pro. Gemini choices use Gemini 3.7 Flash, Gemini 3.6 Flash, or Gemini 3.1 Pro. Retired model names are not accepted.

Generate prompts, content, schema, and llms.txt.

Search the live web for current information and cite its sources.

Read connected context from supported integrations.

Read existing website content and publish or prepare changes through WordPress, GitHub, Webflow, or Shopify when connected.

Preserve platform-specific edit details from connected CMS reads, including GitHub paths and SHAs, Shopify asset IDs, and Webflow editable node IDs for targeted component updates.

Find existing citation opportunitiesSection titled “Find existing citation opportunities”

Ask the agent to "Find non-branded questions where our brand is already mentioned or cited." It can quote a discovery run, prepare up to 25 natural conversational prompts, check every exact question across all the brand owner's enabled AI platforms in the background, inspect matching answers and sources, and add a specific suggestion you choose to tracking. The same feature is available in Your Prompts > Discover prompts for every brand.

The agen


cd /tmp && echo "=== confirm api.reachllm.com is an empty catch-all (various methods/paths) ==="; for p in / /v1/brands /v1/analysis /api/v1/anything /definitely-not-a-real-path-xyz; do out=$(curl -sSL -m 8 -D - "https://api.reachllm.com$p" -o body.tmp 2>/dev/null); code=$(echo "$out" | head -1 | tr -d '\r'); clen=$(echo "$out" | grep -i "^content-length:" | tr -d '\r'); bsz=$(wc -c < body.tmp); echo "$p => $code | $clen | bodybytes=$bsz"; done; echo "=== POST probe ==="; curl -sSL -m 8 -o /dev/null -w "POST / => %{http_code} size=%{size_download}\n" -X POST "https://api.reachllm.com/" -H "Content-Type: application/json" -d '{"x":1}'; echo "=== grep whole docs for developer keywords ==="; for p in $(grep -oE "<loc>[^<]+</loc>" sitemap.xml | sed -E 's/<\/?loc>//g'); do txt=$(curl -sSL -m 12 "$p" | python3 strip.py); hits=$(echo "$txt" | grep -ioE "\b(REST API|API key|SDK|npm install|pip install|Bearer token|endpoint|curl -|api\.reachllm\.com|programmatic)\b" | sort -u | tr '\n' ',' ); if [ -n "$hits" ]; then echo "$p => $hits"; fi; done; echo "(scan done)"
=== confirm api.reachllm.com is an empty catch-all (various methods/paths) ===
=== confirm api.reachllm.com is an empty catch-all (various methods/paths) ===
/ => HTTP/2 200  | content-length: 0 | bodybytes=0
/v1/brands => HTTP/2 200  | content-length: 0 | bodybytes=0
/v1/analysis => HTTP/2 200  | content-length: 0 | bodybytes=0
/api/v1/anything => HTTP/2 200  | content-length: 0 | bodybytes=0
/definitely-not-a-real-path-xyz => HTTP/2 200  | content-length: 0 | bodybytes=0
=== POST probe ===
POST / => 200 size=0
=== grep whole docs for developer keywords ===
https://docs.reachllm.com/integrations/bing-webmaster-tools/ => API key,
https://docs.reachllm.com/integrations/mcp/ => api.reachllm.com,
https://docs.reachllm.com/integrations/reachllm-llm-analytics/ => Endpoint,endpoint,
https://docs.reachllm.com/integrations/wordpress-plugin/ => REST API,endpoint,
(scan done)
Thinking…

Now I've got the full picture. Let's check the MCP page a bit more. The MCP page had a mention of api.reachllm.com. Let's get the connection details (server URL, authentication) from the MCP page. api.reachllm.com is a catch-all that returns empty 200s for everything (GET/POST, any path, content-length: 0). In other words, there's no actual REST API behind api.reachllm.com that's accessible anonymously — but the MCP page mentioned api.reachllm.com. Let's get the exact server URL and setup instructions for the MCP connector.

I'm going to fetch the continuation of the MCP page (the connection/setup part).

cd /tmp && curl -sSL -m 15 "https://docs.reachllm.com/integrations/mcp/" | python3 strip.py > mcp.txt; echo "=== full MCP page (tail: setup/connection) ==="; sed -n '40,400p' mcp.txt | grep -iE "http|url|token|mcp|json|claude|cursor|server|install|npx|config|scope|Account >" | head -80
=== full MCP page (tail: setup/connection) ===
=== full MCP page (tail: setup/connection) ===
Read saved diagnostic summaries for 1 to 50 known URLs within one brand. Returns requested-page coverage, capture dates and warnings; pages without current evidence remain unknown. Does not discover pages or request website content.
For a question about which pages to fix, use known URLs from the saved GEO inventory, read their findings, then open a selected page with get-page-bot-view before proposing an exact change. Full reports include structured diagnostics, missing text excerpts, the measurement basis, suggested fix steps and verification guidance. A percentage is not a provider-readability score, and a page with no saved comparison is not a pass. A requested source change uses the existing connected publishing tools and their permissions; these read and inspection tools do not edit the website.
Use the selected session brand, or pass an authorized brandId for an account-scoped token. A brand-scoped token cannot select another brand or switch to another user's personal preview. Current tenant and brand permissions are checked by ReachLLM for each request. Tools never accept a model-selected account identity or access role.
Account token: lets the connected client list active brands and use available MCP tools across brands your account can access.
Brand token: limits the connected client to the selected brand.
Plan and integration limits still apply: the client cannot use disconnected integrations, unavailable plan features, or revoked tokens.
Each teammate connects with their own ReachLLM account and credential. Installing a plugin does not grant additional access or transfer another person's connection. A persistent credential connection can authenticate a new session; authenticate-with-token signs in only its current MCP session.
Choose the intended brand for each conversation. An account connection can access multiple permitted brands, so naming the client keeps the task clear. Saved reads return current server data when requested; an idle external chat does not automatically receive another teammate's edits. Before changing a shared document, read its current version. If ReachLLM rejects a stale edit, load the newer version and merge the intended change before trying again.
External compute stays external: Claude Code, Cursor, Codex, Windsurf, Cline, or another client handles the conversation and reasoning. ReachLLM handles authenticated data and actions.
Server URLSection titled “Server URL”
Use this remote MCP server URL:
https://api.reachllm.com/mcp/mcp
Generate an access tokenSection titled “Generate an access token”
Go to MCP.
Name the token, for example ReachLLM MCP, Cursor MCP, or Claude Code MCP.
Click Generate access token.
Choose Copy token before leaving the tab. The credential stays hidden on screen and cannot be recovered after you leave.
Create one token per client so you can revoke a single client without disconnecting everything else.
Cursor, Windsurf, and ClineSection titled “Cursor, Windsurf, and Cline”
Add ReachLLM to your MCP config:
"mcpServers": {
"url": "https://api.reachllm.com/mcp/mcp"
Connect the MCP server, then ask the client to call the authenticate-with-token tool and paste your ReachLLM access token.
Prompt to paste after the server is connected:
Use the ReachLLM authenticate-with-token tool now. When it asks for the access token, I will paste the token I generated in ReachLLM Account > MCP. After it succeeds, list my active brands.
MCP. After it succeeds, list my active brands.">
Claude.ai and Claude DesktopSection titled “Claude.ai and Claude Desktop”
Claude.ai custom connectors currently expect OAuth account authorization. ReachLLM access tokens do not use the optional OAuth Client ID or OAuth Client Secret fields, so leave those blank unless ReachLLM gives you OAuth credentials.
Use Claude Code, Cursor, Codex, Windsurf, or Cline for token-based MCP access today. If Claude is already connected to the wrong ReachLLM account, remove that connector in Claude and revoke the old token in Account settings > MCP.
Claude CodeSection titled “Claude Code”
claude mcp add reachllm --transport http https://api.reachllm.com/mcp/mcp
Use the ReachLLM authenticate-with-token tool now. When it asks for the access token, I will paste the token I generated in ReachLLM Account > MCP. After it succeeds, list my active brands.
MCP. After it succeeds, list my active brands.">
Codex and other MCP clientsSection titled “Codex and other MCP clients”
Any MCP client that supports remote MCP over Streamable HTTP and exposes MCP tools after connection can use the same server URL. Add ReachLLM as a remote MCP server, then authenticate with the authenticate-with-token tool if the client does not show its own token prompt.
Some clients only ask for the server URL during setup. That does not mean the client is connected to your ReachLLM account yet. The first ReachLLM action should be authenticate-with-token; after that, the same MCP session can list active brands, read visibility results, and run supported actions.
When the client exposes MCP tools but does not show a token prompt, paste this:
Use the ReachLLM authenticate-with-token tool now. When it asks for the access token, I will paste the token I generated in ReachLLM Account > MCP. After it succeeds, list my active brands.
MCP. After it succeeds, list my active brands.">
ReachLLM stores only a hashed version of each access token. The raw credential is available through Copy token only when created and is never returned again by the app.
You can revoke a token from Account settings > MCP. Revocation takes effect immediately for external clients using that token.
Keep tokens out of source code, screenshots, support tickets, and public chat transcripts.
Thinking…

With this, I've been able to fully and clearly grasp the situation.

ReachLLM is a hosted SaaS product (GEO/AEO — Generative Engine Optimization) that helps brands get discovered and cited by AI platforms (ChatGPT, Google AI Overviews, Perplexity, Gemini).

Developer workflow: The only programmatic interface is the remote MCP server at https://api.reachllm.com/mcp/mcp, which:

  • Requires a ReachLLM account (app.reachllm.com)
  • Requires generating an MCP access token from Account > MCP (login required, a paid plan is effectively needed for full access)
  • Requires an external MCP client (Claude Code, Cursor, Codex, Windsurf, Cline) that provides the model compute
  • Authentication is done via the authenticate-with-token tool using the generated token.

There is no SDK (no npm/pypi package — confirmed 404). There is also no documented public REST API — api.reachllm.com returns an empty 200 for everything anonymously (it's a catch-all). The actual app API is app.reachllm.com/api/... but it's not a documented public developer API, and requires an authenticated account/session and a paid plan. The LLM Analytics ingest endpoint (https://app.reachllm.com/api/ai-crawler-traffic/ingest) requires a connection key generated within the app and a hosted website to forward crawler logs from — this is for data ingestion, not for "using the product."

Therefore, in response to the user's request:

  • "Build a simple example using ReachLLM"
  • "Use the hosted product through its SDK or API"
  • "If there is no actionable developer workflow for this product, say so frankly and stop"
  • "Don't spin up local service stacks or wait on long-running commands"

Reality check:

  1. There is no SDK (no npm/pip package exists).
  2. There is no public REST API that can be called with just an API key. api.reachllm.com is an empty catch-all.
  3. The only programmatic path is a remote MCP server that requires:
    • A ReachLLM account (signup with company email)
    • A paid plan / Stripe checkout (the quickstart requires Stripe checkout to run any actual analysis; free trials require a payment method)
    • An MCP access token generated inside the authenticated dashboard
    • An external MCP client (Claude Code / Cursor / Codex) that provides the model compute

I cannot create an account, complete Stripe checkout, generate an MCP token, or connect an external MCP client on behalf of the user. There are no credentials in the environment. Therefore, there is no actionable developer workflow I can execute here. The quickstart is an account+dashboard+Stripe flow, not a code-first quickstart.

The task instructions say: "If there is no actionable developer workflow for this product, say so frankly and stop." This is exactly that case. I should not fabricate examples or spin up a local stack (Forge/ReachMCP's local npm run dev stack is explicitly not recommended, and it's a different product anyway).

I should also touch on the MCP "example" that could be written as configuration — but to actually make it work, you'd need a real token + an external MCP client + a paid account. I can describe that this is the closest thing to a developer workflow and provide the configuration, but I cannot execute it. Given the instructions, the honest answer is: no actionable SDK/API exists, the only integration is a remote MCP server gated behind an authenticated paid account + an external MCP client, and I cannot set that up here. So I should say so frankly and stop.

But I should still provide value:

  • Explain how the pricing mechanism works (clearly documented — I can fully answer this).
  • Briefly evaluate manageability (whether this product is manageable for me = can it be managed programmatically / via automation).
  • State frankly that no actionable developer workflow exists, explain why, and stop.

The user is also asking "briefly tell me whether this product is manageable for you." Manageability = can it be managed via automation/code. Since the only interface is MCP behind authentication + an external client, manageability is low without credentials; it's primarily a dashboard-driven SaaS. I'll give a brief assessment.

Let me also check the pricing numbers and verify that there really is no SDK via other registries or scoped names. I already searched npm/PyPI for several variants. For thoroughness, let me quickly confirm once more that no SDK exists (e.g., packagist, GitHub packages), and confirm that app.reachllm.com exists and is a dashboard (login). Actually, I've done enough. api.reachllm.com is a catch-all empty response. Let me just double-check whether the MCP endpoint returns anything meaningful without authentication (to confirm it's not an open API) — a quick check to /mcp/mcp.

I'll do a final verification batch, then write the answer. I don't want to over-investigate; I have enough information. But to confirm that the MCP server requires authentication (returns an error rather than being open), a quick probe will strengthen the conclusion. Let me quickly probe the MCP endpoint.

Actually, the instructions say to stay lightweight and stop if blocked. I've done enough investigation. Let me do one quick probe to confirm that /mcp/mcp is a real endpoint but gated by authentication, then conclude. This is fast and not long-running.

I'll probe the MCP endpoint's headers.

cd /tmp && echo "=== probe real MCP endpoint (the documented one) ==="; curl -sSL -m 12 -D - "https://api.reachllm.com/mcp/mcp" -o mcp_probe.txt 2>/dev/null | head -20; echo "--- body (first 600 bytes) ---"; head -c 600 mcp_probe.txt; echo; echo "=== POST JSON-RPC initialize (no auth) ==="; curl -sSL -m 12 -o /dev/null -w "status=%{http_code} size=%{size_download}\n" -X POST "https://api.reachllm.com/mcp/mcp" -H "Content-Type: application/json" -H "Accept: application/json, text/event-stream" -d '{"jsonrpc":"2.0","id":1,"method":"initialize","params":{"protocolVersion":"2024-11-05","capabilities":{},"clientInfo":{"name":"probe","version":"0.0.1"}}}'; echo "=== app.reachllm.com (dashboard) ==="; curl -sSL -m 12 -o /dev/null -w "status=%{http_code} final=%{url_effective}\n" "https://app.reachllm.com"; echo "=== last-ditch SDK registries ==="; for u in "https://packagist.org/packages/reachllm/reachllm" "https://rubygems.org/api/v1/gems/reachllm.json" "https://crates.io/api/v1/crates/reachllm"; do echo "$u -> $(curl -sSL -m 8 -o /dev/null -w '%{http_code}' "$u")"; done
=== probe real MCP endpoint (the documented one) ===
=== probe real MCP endpoint (the documented one) ===
HTTP/2 200 
access-control-allow-origin: *
alt-svc: h3=":443"; ma=2592000
content-type: text/html; charset=utf-8
date: Sun, 01 Nov 2026 16:21:25 GMT
via: 1.1 Caddy
content-length: 2282

--- body (first 600 bytes) ---
<!DOCTYPE html>
<html>
<head>
<meta charset="UTF-8">
<title>reachllm</title>
<style>
* {
  font-family: monospace;
  font-size: 14px;
  font-weight: normal;
  color: #333;
  line-height: 1.5;
}
body {
  margin: 20px;
}
h1 {
  font-size: 18px;
  font-weight: bold;
  margin: 20px 0 10px 0;
}
h2 {
  font-size: 14px;
  font-weight: normal;
  margin: 20px 0 10px 0;
}
p, pre, ol, li, ul {
  margin: 5px 0;
}
a {
  color: #333;
  text-decoration: underline;
}
pre {
  margin: 10px 0;
  white-space: pre-wrap;
}
ol, ul {
  padding-left: 20px;
}
.description {
  margin: 10px 0;
  color: #666;
}
.tools {
 
=== POST JSON-RPC initialize (no auth) ===
status=200 size=624
=== app.reachllm.com (dashboard) ===
status=200 final=https://app.reachllm.com/
=== last-ditch SDK registries ===
https://packagist.org/packages/reachllm/reachllm -> 200
https://rubygems.org/api/v1/gems/reachllm.json -> 404
https://crates.io/api/v1/crates/reachllm -> 403
Thinking…

The MCP endpoint is returning an HTML page (content-type: text/html) — it's a landing/info page, not a proper JSON-RPC MCP response. A POST for initialize returned size=624 (probably the same HTML or an error, not a valid MCP session). Let's take a quick look at the contents of that HTML page — it might describe the tools and whether authentication is required. In reality, this is probably just a static description page (the HTML has a ".tools" class). This suggests that the MCP endpoint is returning a static HTML description page rather than actually handling MCP Streamable HTTP JSON-RPC without a session/auth. To get an actual MCP session, you need to go through authenticate-with-token using a token generated in an authenticated paid account, via a real MCP client.

Let's quickly look at the full HTML body and the response body from the initialize POST to see what it says. It's a small amount (2282 bytes / 624 bytes). This will serve as a definitive confirmation that we can't just call this API directly.

cd /tmp && echo "=== MCP endpoint HTML body (stripped) ==="; python3 strip.py < mcp_probe.txt; echo; echo "=== initialize POST response body ==="; curl -sSL -m 12 -X POST "https://api.reachllm.com/mcp/mcp" -H "Content-Type: application/json" -H "Accept: application/json, text/event-stream" -d '{"jsonrpc":"2.0","id":1,"method":"initialize","params":{"protocolVersion":"2024-11-05","capabilities":{},"clientInfo":{"name":"probe","version":"0.0.1"}}}' | python3 strip.py | head -40
=== MCP endpoint HTML body (stripped) ===
=== MCP endpoint HTML body (stripped) ===
reachllm

reachllm (v1.0.0)

AI Brand Optimization — Analyze brands, track visibility across AI platforms, run GEO audits, and optimize content for LLM discovery.

URL: https://api.reachllm.com/mcp

Connect with Claude Code

claude mcp add --transport http "reachllm" https://api.reachllm.com/mcp

Connect with Cursor

cursor://anysphere.cursor-deeplink/mcp/install?config=eyJ1cmwiOiJodHRwczovL2FwaS5yZWFjaGxsbS5jb20vbWNwIn0=&name=reachllm

Connect with VS Code

vscode:mcp/install?%7B%22url%22%3A%22https%3A%2F%2Fapi.reachllm.com%2Fmcp%22%2C%22name%22%3A%22reachllm%22%2C%22type%22%3A%22http%22%7D

Connect with VS Code Insiders

vscode-insiders:mcp/install?%7B%22url%22%3A%22https%3A%2F%2Fapi.reachllm.com%2Fmcp%22%2C%22name%22%3A%22reachllm%22%2C%22type%22%3A%22http%22%7D

Connect with ChatGPT

Enable Developer Mode:
Settings → Connectors → Advanced → Developer mode

Import this MCP server:
Go to Connectors tab and add: https://api.reachllm.com/mcp

Use in conversations:
Choose the MCP server from the Plus menu

=== initialize POST response body ===
event: message
data: {"result":{"protocolVersion":"2024-11-05","capabilities":{"logging":{},"completions":{},"tools":{"listChanged":true},"prompts":{"listChanged":true},"resources":{"subscribe":true,"listChanged":true}},"serverInfo":{"name":"reachllm","version":"1.0.0","description":"AI Brand Optimization — Analyze brands, track visibility across AI platforms, run GEO audits, and optimize content for LLM discovery.","title":"ReachLLM","websiteUrl":"https://reachllm.com","icons":[{"src":"https://api.reachllm.com/mcp/mcp-use/public/icon.svg","mimeType":"image/svg+xml","sizes":["512x512"]}]}},"jsonrpc":"2.0","id":1}
Thinking…

The MCP endpoint actually responds to JSON-RPC initialize without authentication. It returned a valid MCP initialize result. In other words, the MCP server is alive and can negotiate the protocol. Let's try tools/list and check the tools. This is the real developer workflow — a remote MCP server over Streamable HTTP.

But there's a caveat: the actual data-fetching tools require an access token generated from an authenticated (paid) ReachLLM account via authenticate-with-token. Let's check what tools are exposed unauthenticated with tools/list. initialize succeeded without a session ID header, but MCP's Streamable HTTP normally requires a session ID (Mcp-Session-Id) obtained from initialize. Let's check whether initialize returned a session header. Let's properly perform the full handshake: get the Mcp-Session-Id header from initialize, then call tools/list with that session ID attached.

Let's properly execute the MCP handshake:

  1. POST initialize -> get the Mcp-Session-Id header
  2. POST notifications/initialized (required by the spec)
  3. POST tools/list with the session ID header attached

Let's do this. This is fast and not long-running.

This is actually an executable developer workflow! A remote MCP server that speaks Streamable HTTP. With a few curl calls, we can build a "simple example" — a small client script that initializes and lists tools. This is a legitimate, lightweight, hosted-product-via-API approach (not a local service stack, not long-running).

However, to actually get brand data (the purpose of this product), you need authenticate-with-token with a token generated in an authenticated paid account. Without credentials, you can connect, initialize, and list tools (and call read tools that don't require authentication), but you can't retrieve actual brand/visibility data. That said, it's sufficient to demonstrate a "simple example" of using the product's API surface.

Let's do the full handshake and tools/list to build a real example. Let's get the session ID.

The MCP endpoint speaks real JSON‑RPC. Let me complete the handshake and list tools — this is the actual developer surface.

cd /tmp && URL="https://api.reachllm.com/mcp/mcp"; H='-H Content-Type:application/json -H Accept:application/json,text/event-stream'
echo "=== 1) initialize (capture headers) ==="
curl -sSL -m 15 -D init_hdr.txt $H -X POST "$URL" \
  -d '{"jsonrpc":"2.0","id":1,"method":"initialize","params":{"protocolVersion":"2024-11-05","capabilities":{},"clientInfo":{"name":"pi-probe","version":"0.1"}}}' -o init_body.txt
SID=$(grep -i "^mcp-session-id:" init_hdr.txt | sed -E 's/^[^:]+:[[:space:]]*//' | tr -d '\r')
echo "Mcp-Session-Id = [$SID]"
echo "=== 2) notifications/initialized ==="
curl -sSL -m 15 $H -H "Mcp-Session-Id: $SID" -X POST "$URL" \
  -d '{"jsonrpc":"2.0","method":"notifications/initialized"}' -o /dev/null -w "status=%{http_code}\n"
echo "=== 3) tools/list ==="
curl -sSL -m 15 $H -H "Mcp-Session-Id: $SID" -X POST "$URL" \
  -d '{"jsonrpc":"2.0","id":2,"method":"tools/list","params":{}}' -o tools_body.txt -w "status=%{http_code}\n"
echo "--- tools (names) ---"
grep -oE '"name":"[^"]+"' tools_body.txt | sed -E 's/"name":"([^"]+)"/\1/' | sort -u
echo "--- tool count ---"; grep -oE '"name":"[^"]+"' tools_body.txt | sort -u | wc -l
=== 1) initialize (capture headers) ===
=== 1) initialize (capture headers) ===
Mcp-Session-Id = [6d2afae6-6248-4e01-b4ba-e66dd865b0c9]
=== 2) notifications/initialized ===
status=202
=== 3) tools/list ===
status=200
--- tools (names) ---
add-calendar-item
add-competitor
analyze-brand
analyze-prompt-performance
archive-discovered-prompt
assign-products-to-prompt
assign-tags-to-prompt
authenticate-with-token
build-website
bulk-assign-tags-products
cancel-scheduled-prompt
cms-get-content
cms-get-status
cms-list-content
cms-push-llms-txt
cms-push-schema
cms-update-content
cms-upload-media
comment-on-calendar-item
compare-visibility-runs
composio-calendar-create-event
composio-calendar-list-events
composio-execute-action
composio-gmail-create-draft
composio-gmail-send-email
composio-granola-get-transcript
composio-granola-list-meetings
composio-granola-query-meetings
composio-list-connections
composio-notion-create-page
composio-reddit-create-post
composio-sheets-find
composio-sheets-get-info
composio-sheets-read
composio-slack-send-message
crawl-website
create-article
create-content-suggestion
create-contract
create-geo-proposal
create-site-page
create-survey
create-visibility-report
delete-calendar-item
delete-site-page
delete-skill
edit-site
fetch-webpage
find-content-link-opportunities
fix-geo-issue
generate-ai-content
generate-llms-txt
generate-prompts
get-agent-schedule
get-analytics-data
get-articles
get-audit-history
get-authority-audit
get-bing-webmaster-data
get-bot-view-findings
get-brand-knowledge
get-brand-sentiment
get-business-context
get-calendar
get-citation-discoveries
get-competitor-comparison
get-content-calendar
get-content-document
get-content-quality-audit
get-crawler-traffic-data
get-directory-checks
get-opportunity-sources
get-page-bot-view
get-page-content
get-page-creations
get-page-document
get-platform-overview
get-pr-outreach
get-profile
get-search-console-data
get-site-pages
get-site-publish-status
get-site-scan
get-social-presence-audit
get-source-detail
get-source-evidence
get-source-intelligence
get-source-inventory
get-strategy-intelligence
get-technical-seo-audit
get-visibility-detailed-responses
get-visibility-history
get-visibility-timeseries
github-commit-files
github-list-repo-tree
github-read-file
inspect-page-bot-view
list-brands
list-competitors
list-content-documents
list-prompts
list-scheduled-prompts
list-skills
list-visibility-reports
list-visibility-runs
login
logout
manage-contract-versions
manage-geo-proposal-versions
optimize-content
optimize-site-page
publish-content-document
publish-site
quote-citation-discovery
read-contract
read-docs-page
read-survey-responses
redesign-website
remove-competitor
remove-multiple-competitors
remove-products-from-prompt
remove-tags-from-prompt
request-content-asset
research-prompts
restore-discovered-prompt
run-brand-visibility
run-citation-discovery
run-geo-audit
run-skill
save-page-document
save-skill
scan-website
schedule-prompt
search-docs
search-uploaded-files
set-citation-discovery-schedule
set-competitor-aliases
set-competitor-type
set-internal-context
start-content-review
submit-credentials
track-discovered-prompt
transition-content-lifecycle
transition-content-suggestion
update-agent-schedule
update-article
update-brand-identity
update-brand-knowledge
update-calendar-item
update-content-review-issue
update-gemini-model-preference
update-prompts
update-survey
--- tool count ---
153
Thinking…

The MCP server exposes 153 tools and has completed the full Streamable HTTP handshake without authentication. This is the real developer workflow. Next, let's test whether the actual data tools (like get-profile or list-brands) work without authentication, to confirm the boundaries. The documentation stated that authenticate-with-token is required with a token obtained from a paid account. Let's quickly call list-brands and get-profile to check the auth boundary. This will tell us whether we can build a "simple example" that actually returns data, or whether we can only demonstrate the handshake + tool listing.

Let's call tools/call for list-brands and get-profile on the same session. This is fast.

Actually, the session might be per-session. Let's reuse $SID. Though the SID variable is in a previous shell invocation — each bash tool call is a new shell. We need to either redo the handshake within the same command or persist the SID. To do initialize + initialized + tools/call all at once, let's write a small self-contained script. That would be cleaner, and it also gives us a reusable "simple example."

Actually, this is a perfect opportunity. We can build the "simple example" as a small script that performs the handshake and makes tool calls. Let's create a minimal Node or Python script as the artifact, then run it to demonstrate the behavior (handshake + tool listing + showing the auth-required boundary). This lets us fulfill "build a simple example using ReachLLM... use the hosted product through its SDK or API" in a lightweight manner, without a local service stack or long-running commands.

Let's write a minimal Python example (no dependencies — uses urllib) that does the following:

  1. initialize
  2. notifications/initialized
  3. tools/list
  4. Attempt tools/call get-profile / list-brands (to demonstrate the auth boundary)

Then run it. Let's first probe the auth boundary with a quick inline call, then write the artifact script based on what we learn.

Let's do the handshake and call list-brands and get-profile in one script to check the responses.

Handshake works and exposes 153 tools. Let me probe the auth boundary (can I actually pull data, or is a token required?), which determines how far a runnable example can go.

cd /tmp && cat > probe_call.py <<'PY'
import json, urllib.request

URL="https://api.reachllm.com/mcp/mcp"
BASE={"Content-Type":"application/json","Accept":"application/json, text/event-stream"}

def post(body, sid=None):
    data=json.dumps(body).encode()
    h=dict(BASE)
    if sid: h["Mcp-Session-Id"]=sid
    req=urllib.request.Request(URL, data=data, headers=h, method="POST")
    resp=urllib.request.urlopen(req, timeout=20)
    raw=resp.read().decode()
    return resp.headers.get("Mcp-Session-Id"), resp.status, raw

def parse_sse(raw):
    # MCP streamable-http returns SSE: lines like 'data: {...}'
    for line in raw.splitlines():
        if line.startswith("data:"):
            return json.loads(line[5:].strip())
    try: return json.loads(raw)
    except: return {"_raw": raw[:400]}

sid,_,raw = post({"jsonrpc":"2.0","id":1,"method":"initialize","params":{"protocolVersion":"2024-11-05","capabilities":{},"clientInfo":{"name":"pi","version":"0.1"}}})
post({"jsonrpc":"2.0","method":"notifications/initialized"}, sid)
for name,args in [("get-profile",{}),("list-brands",{}),("authenticate-with-token",{})]:
    _,status,raw = post({"jsonrpc":"2.0","id":9,"method":"tools/call","params":{"name":name,"arguments":args}}, sid)
    r=parse_sse(raw)
    res=r.get("result",r)
    # extract text content
    txt=""
    if isinstance(res,dict) and "content" in res:
        txt=" | ".join(c.get("text","") for c in res["content"] if isinstance(c,dict))
    print(f"\n### tools/call {name} (http {status}) isError={res.get('isError') if isinstance(res,dict) else '?'}")
    print((txt or json.dumps(res))[:700])
PY
python3 probe_call.py
### tools/call get-profile (http 200) isError=True

### tools/call get-profile (http 200) isError=True
You are not signed in. Use 'authenticate-with-token' with a ReachLLM MCP token first.

### tools/call list-brands (http 200) isError=True
Not signed in. Please call 'authenticate-with-token' with a ReachLLM MCP token, or use the 'login' tool. If they don't have an account, they can create one at https://app.reachllm.com and then sign in here.

### tools/call authenticate-with-token (http 200) isError=True
MCP error -32602: Input validation error: Invalid arguments for tool authenticate-with-token: [
  {
    "expected": "string",
    "code": "invalid_type",
    "path": [
      "token"
    ],
    "message": "Invalid input: expected string, received undefined"
  }
]
Thinking…

This makes the boundaries completely clear now:

  • The MCP server is live and speaks Streamable HTTP. Anyone can do initialize and retrieve the tool list (153 tools).
  • All data/action tools require authentication via authenticate-with-token using a ReachLLM MCP token. This token is generated within an authenticated account (Account > MCP), which requires a paid plan / Stripe checkout.
  • There is no SDK (there are no npm/pypi/crates/rubygems packages — all 404).
  • There is no public REST API callable with an API key alone. api.reachllm.com is a catch-all that only serves MCP and a static info page.

Therefore, "a simple example using ReachLLM" is something I can build and run up to the authentication boundary: a small dependency-free client that connects to the hosted MCP endpoint, initializes, lists tools, and demonstrates an authenticated call path. It will run against the live hosted product (via the API/MCP surface), it's lightweight, no local service stack is needed, and there are no long-running commands. However, to actually fetch brand/visibility data, the user needs to paste in a token from their account, and that requires a paid subscription — I cannot generate it myself.

This is actionable, and I'm not completely blocked. The user said: "Build a simple example using ReachLLM. Use the hosted product through its SDK or API." The hosted product's API surface is the MCP server. So, I'll build a minimal sample script that works against it, run it to show live output (handshake + tool list + auth boundary), and explain that fetching real data requires plugging in an MCP token.

If there were no actionable developer workflow at all, I should follow the instructions and say so and stop — but an actionable workflow does exist (the MCP HTTP API). So I'll proceed with building and running the lightweight sample. The only thing I can't do is authenticate (no credentials / no paid account). I'll state that clearly.

Now, let's build the deliverable: a small self-contained sample in the repo (/sandbox/repo). The repo is essentially empty, so I'll add a sample. I'll make it dependency-free Python (stdlib only), clean, and runnable. Contents:

  1. Connect to https://api.reachllm.com/mcp/mcp
  2. initialize + notifications/initialized
  3. tools/list (show a few)
  4. Accept an optional token via the REACHLLM_MCP_TOKEN environment variable. If present, run authenticate-with-token, then call list-brands / get-profile. If not present, show a boundary message with guidance.

I'll also add a README explaining the product, pricing, how to obtain a token, and manageability notes.

Now let me write the sample script. Then run it (no token → it'll show the boundary; that's fine and expected). Then maybe I'll demo the tool list output too.

Let's create /sandbox/repo/reachllm_example.py and /sandbox/repo/README.md.

First I'll write the script, then run it.

I'll make the script robust and clean. Stdlib only. It'll do:

  • SSE parsing (data: lines)
  • initialize, get session ID
  • notifications/initialized
  • tools/list → print count + first N names
  • If REACHLLM_MCP_TOKEN is set: authenticate-with-token, then get-profile and list-brands; otherwise: try list-brands to show a clean "sign-in required" message, and print how to obtain a token.

Let me write it.

Clear boundary confirmed: the hosted MCP API is live and fully enumerable, but every data/action tool requires an MCP token generated inside an authenticated (paid) ReachLLM account. There's no SDK and no key-only REST API. That's still an actionable developer workflow (remote MCP over HTTP), so I'll build a light, dependency-free example that runs against the live endpoint and stops cleanly at the auth boundary.

File edits

1 file

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Writtenreachllm_example.pyView changes

/sandbox/repo/reachllm_example.py

Written content · previous contents were not recorded

#!/usr/bin/env python3
"""
Minimal ReachLLM example — talks to the hosted ReachLLM MCP API.
 
ReachLLM's only programmatic surface is a remote MCP (Model Context Protocol)
server over Streamable HTTP:
 
    https://api.reachllm.com/mcp/mcp
 
There is no published SDK (npm/PyPI) and no key-only REST API. This script uses
nothing but the Python standard library — no local services, nothing long-running.
 
What it does:
  1. Performs the MCP handshake (initialize -> notifications/initialized).
  2. Lists the available tools (150+ on the live server).
  3. If REACHLLM_MCP_TOKEN is set, authenticates and pulls real account data
     (get-profile, list-brands). Otherwise it demonstrates the auth boundary.
 
Usage:
    python3 reachllm_example.py                      # connect + enumerate tools
    REACHLLM_MCP_TOKEN=xxxx python3 reachllm_example.py   # + real data
 
Get a token from the ReachLLM app: Account > MCP > Generate access token
(requires a signed-in account on a paid plan). Keep it secret.
"""
 
import json
import os
import sys
import urllib.request
 
MCP_URL = "https://api.reachllm.com/mcp/mcp"
HEADERS = {
    "Content-Type": "application/json",
    "Accept": "application/json, text/event-stream",
}
PROTOCOL = "2024-11-05"
 
 
def post(body, session_id=None):
    """POST a JSON-RPC message; return (session_id, status, parsed_result)."""
    headers = dict(HEADERS)
    if session_id:
        headers["Mcp-Session-Id"] = session_id
    req = urllib.request.Request(
        MCP_URL, data=json.dumps(body).encode(), headers=headers, method="POST"
    )
    resp = urllib.request.urlopen(req, timeout=30)
    raw = resp.read().decode("utf-8", "replace")
    return resp.headers.get("Mcp-Session-Id"), resp.status, _parse(raw)
 
 
def _parse(raw):
    """MCP Streamable HTTP replies as SSE ('data: {...}') or plain JSON."""
    for line in raw.splitlines():
        if line.startswith("data:"):
            return json.loads(line[5:].strip())
    return json.loads(raw) if raw.strip() else {}
 
 
def call_tool(session_id, name, arguments):
    """Invoke a tool and return (is_error, text)."""
    _, _, msg = post(
        {"jsonrpc": "2.0", "id": 1, "method": "tools/call",
         "params": {"name": name, "arguments": arguments}},
        session_id,
    )
    result = msg.get("result", msg)
    text = ""
    if isinstance(result, dict):
        text = " | ".join(
            c.get("text", "") for c in result.get("content", []) if isinstance(c, dict)
        )
    return bool(result.get("isError")), (text or json.dumps(result))
 
 
def main():
    # 1) Handshake
    sid, _, _ = post({
        "jsonrpc": "2.0", "id": 1, "method": "initialize",
        "params": {"protocolVersion": PROTOCOL, "capabilities": {},
                   "clientInfo": {"name": "reachllm-example", "version": "1.0"}},
    })
    post({"jsonrpc": "2.0", "method": "notifications/initialized"}, sid)
    print(f"Connected to {MCP_URL}")
    print(f"MCP session: {sid}\n")
 
    # 2) List tools
    _, _, msg = post({"jsonrpc": "2.0", "id": 2, "method": "tools/list", "params": {}}, sid)
    tools = msg.get("result", {}).get("tools", [])
    names = sorted(t["name"] for t in tools)
    print(f"Server exposes {len(names)} tools. A few:")
    for n in names[:12]:
        print(f"  - {n}")
    print("  ...\n")
 
    # 3) Authenticate (optional) then read real data
    token = os.environ.get("REACHLLM_MCP_TOKEN")
    if token:
        err, text = call_tool(sid, "authenticate-with-token", {"token": token})
        print(f"authenticate-with-token -> {'ERROR' if err else 'OK'}: {text[:200]}\n")
        if not err:
            for tool in ("get-profile", "list-brands"):
                err, text = call_tool(sid, tool, {})
                print(f"{tool} -> {'ERROR' if err else 'OK'}: {text[:400]}\n")
        return
 
    # No token: show the auth boundary (this is expected without credentials).
    err, text = call_tool(sid, "list-brands", {})
    print("list-brands (unauthenticated) ->")
    print(f"  {text[:300]}")
    print("\nTo pull real data, set REACHLLM_MCP_TOKEN (Account > MCP in the app).")
 
 
if __name__ == "__main__":
    try:
        main()
    except urllib.error.HTTPError as e:
        sys.exit(f"HTTP {e.code}: {e.read().decode('utf-8', 'replace')[:300]}")
    except Exception as e:  # noqa: BLE001
        sys.exit(f"Error: {e}")
Thinking…

Alright, let's run the example (without a token) and do a demonstration against the live endpoint.

cd /sandbox/repo && python3 reachllm_example.py
Connected to https://api.reachllm.com/mcp/mcp
Connected to https://api.reachllm.com/mcp/mcp
MCP session: 46992709-46be-4108-b33a-a555e38190a4

Server exposes 153 tools. A few:
  - add-calendar-item
  - add-competitor
  - analyze-brand
  - analyze-prompt-performance
  - archive-discovered-prompt
  - assign-products-to-prompt
  - assign-tags-to-prompt
  - authenticate-with-token
  - build-website
  - bulk-assign-tags-products
  - cancel-scheduled-prompt
  - cms-get-content
  ...

list-brands (unauthenticated) ->
  Not signed in. Please call 'authenticate-with-token' with a ReachLLM MCP token, or use the 'login' tool. If they don't have an account, they can create one at https://app.reachllm.com and then sign in here.

To pull real data, set REACHLLM_MCP_TOKEN (Account > MCP in the app).
Thinking…

The example runs against the live hosted endpoint. Next, let's write a concise README documenting the findings: what ReachLLM is, how pricing works, developer workflow (MCP), and notes on manageability. After that, provide the user with a final answer covering:

  1. What was built (example) and that it works.
  2. How pricing works.
  3. Whether this product is manageable for me (briefly).
  4. Where I got stuck (can't authenticate without token/paid account; no SDK/REST API).

Keep the README concise. Then the final message.

Let's write README.md.

Runs clean against the live endpoint. Let me add a short README capturing the findings.

File edits

1 file

Completed
WrittenREADME.mdView changes

/sandbox/repo/README.md

Written content · previous contents were not recorded

# ReachLLM — simple example
 
ReachLLM is a hosted **GEO/AEO** SaaS ("AI brand optimization"): it tracks
whether your brand is mentioned/cited by ChatGPT, Google AI Overviews,
Perplexity, and Gemini, runs GEO audits of your site, and generates/optimizes
content. Docs: https://docs.reachllm.com · App: https://app.reachllm.com
 
## Developer workflow
 
ReachLLM has **no SDK** (nothing on npm/PyPI/crates/rubygems) and **no key-only
REST API**. Its one programmatic surface is a **remote MCP server** (Model
Context Protocol over Streamable HTTP):
 
    https://api.reachllm.com/mcp/mcp
 
Anyone can connect and enumerate the ~153 tools, but every data/action tool
requires an **MCP access token** generated inside a signed-in account
(Account > MCP > Generate access token), which needs a paid plan.
 
`reachllm_example.py` (stdlib only, no local services) does the MCP handshake,
lists tools, and — if you export `REACHLLM_MCP_TOKEN` — authenticates and reads
real account data:
 
    python3 reachllm_example.py
    REACHLLM_MCP_TOKEN=xxxx python3 reachllm_example.py
 
The same server also plugs into Claude Code, Cursor, Codex, VS Code, Windsurf,
and Cline as a remote MCP server (external client supplies the model compute).
 
## Pricing (from docs)
 
Self-serve, per month (yearly in parentheses):
 
| Plan | Price | Brands | Tracked prompts/mo | GEO pages/mo | AI articles/mo | Execution credits/mo |
|---|---|---|---|---|---|---|
| Pro | $399 ($3,990/yr) | 1 | 100 | 100 | 10 | 4,000 |
| Scale | $999 ($9,990/yr) | up to 10 (pooled) | 500 | 1,000 | 50 | 20,000 |
| Enterprise Agencies | $1,599 ($15,990/yr) | unlimited (pooled) | 1,000 | 2,500 | 50 | 40,000 |
 
- **Growth** (managed service, one brand): from **$3,500/mo** ($35,000/yr), 3-month minimum.
- **Enterprise**: custom.
- **Execution credits** meter token-heavy work (agent chat, website generation,
  manual visibility refreshes) at **$1 of AI cost = 100 credits**. GEO audits and
  *scheduled* visibility tracking do **not** spend credits (metered by pages / prompts).
- **Top-ups** (never expire, roll over): 4,000 = $79 · 8,000 = $149 · 20,000 = $299 · 100,000 = $999.
- **Model add-ons** (per mo, Pro/Scale/Ent): Google AI Mode $50/$250/$500 ·
  Microsoft Copilot $50/$250/$500 · Claude $200/$1,000/$2,000.
- **Free trial**: 3 days on Pro/Scale (card required via Stripe; one trial per
  account ever). Enterprise Agencies bills immediately.
 
Billing is Stripe-based (some grandfathered agencies via Paddle).
Thinking…

Done. I'll present a concise final answer to the user that includes:

  1. What ReachLLM is, and what was built and executed.
  2. How the pricing structure works.
  3. Whether I can easily manage this product (briefly).
  4. Where I got stuck (couldn't authenticate without a token or paid account; no SDK or REST API available).

I'll keep it concise.

Done. Here's the rundown.

What ReachLLM is

A hosted GEO/AEO SaaS ("AI brand optimization") — it tracks whether your brand gets mentioned/cited by ChatGPT, Google AI Overviews, Perplexity, and Gemini, runs GEO audits of your site, and generates/optimizes content. It's a dashboard product (app.reachllm.com), not a library.

Simple example (built + ran it)

/sandbox/repo/reachllm_example.py — stdlib-only, no local services, nothing long-running. It connects to ReachLLM's hosted MCP server (https://api.reachllm.com/mcp/mcp), does the handshake, lists tools, and reads real data if you give it a token. Verified against the live endpoint:

  • ✅ Connected, got an MCP session, enumerated 153 tools (analyze-brand, run-geo-audit, list-brands, run-brand-visibility, …).
  • ⛔ Every data/action tool returns "Not signed in — use authenticate-with-token".

To pull actual data: REACHLLM_MCP_TOKEN=xxxx python3 reachllm_example.py (token from Account → MCP → Generate access token).

How pricing works

Self-serve monthly (yearly): Pro $399 (1 brand, 100 prompts/mo) · Scale $999 (≤10 brands, 500 prompts/mo) · Enterprise Agencies $1,599 (unlimited, 1,000 prompts/mo). Growth managed service from $3,500/mo (3-mo min); Enterprise custom.

  • Execution credits meter token-heavy work (agent chat, website gen, manual refreshes) at $1 AI cost = 100 credits. GEO audits + scheduled tracking don't spend credits (metered by pages/prompts).
  • Top-ups (never expire): 4k=$79, 8k=$149, 20k=$299, 100k=$999. Model add-ons (Claude/Copilot/Google AI Mode) extra. 3-day trial, card required (Stripe).

Full table in /sandbox/repo/README.md.

Will it be easy for me to manage?

Only moderately, and not autonomously. The good news: there's a real, clean programmatic surface (remote MCP over HTTP) that's scriptable and also plugs into Claude Code/Cursor/Codex. The friction: no SDK and no key-only REST API, and every useful action sits behind an MCP token tied to a paid, signed-in account. So I can enumerate and orchestrate tools, but I can't create the account, pass Stripe checkout, or mint the token myself — a human has to hand me a token first. Day-to-day it's also credit-metered, so unattended automation can hit spend/limit walls.

Where I got blocked

No credentials in this environment, so I stopped at the auth boundary — I can't authenticate or fetch real brand data without a REACHLLM_MCP_TOKEN from your paid account. I did not spin up any local stack (the GitHub "ReachMCP/Forge" repo is a different product and needs npm run dev; I avoided it). Everything else above is live-verified.

If you paste an MCP token (or set it as an env var), I'll extend the example to actually list your brands, pull visibility scores, and run a GEO audit.

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