GHL Coaching MCP Server
Click on "Deploy Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@GHL Coaching MCP ServerShow me a leads overview for broker Sarah"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
GHL Coaching MCP Server
Wraps the GoHighLevel CRM API as MCP tools: contacts, conversations, call transcripts, broker lead overviews, pipelines/opportunities, task creation.
Two entrypoints, same tool logic (shared via tools.js):
File | Transport | Use case |
| stdio | Local use — Claude Desktop, direct CLI testing |
| Streamable HTTP | Remote use — the 365 Yachts WhatsApp bot calls this over the internet |
Setup
npm install
cp .env.example .envFill in .env:
GHL_API_TOKEN,GHL_LOCATION_ID,GHL_COMPANY_ID— from your GHL accountJWT_SECRET— only needed forserver-http.js. Generate a strong random value:node -e "console.log(require('crypto').randomBytes(32).toString('hex'))"This must match the WhatsApp bot's
JWT_SECRETexactly — it's what lets this server verify the signed identity token (name, role, ghlUserId) the bot mints per caller, so per-broker access restrictions inaccess.jshold even against a malicious/confused caller.
Related MCP server: ghl-mcp
Running locally
Stdio (Claude Desktop / CLI):
npm run start:stdioHTTP (what the WhatsApp bot actually talks to):
npm run start:httpBoots on http://localhost:4000 (or $PORT/$MCP_PORT). The tool endpoint is POST /mcp,
and requires Authorization: Bearer <signed JWT> on every request — missing, invalid, expired,
or malformed-identity tokens get a 401.
Testing the HTTP server is reachable
curl http://localhost:4000/
# -> "365 Yachts GHL coaching MCP server (HTTP) is running."Exposing it locally (for testing the WhatsApp bot against this before deploying)
npx ngrok http 4000Use the resulting URL + /mcp as GHL_MCP_URL in the WhatsApp bot's .env.
Note: this needs its own ngrok tunnel, separate from the WhatsApp bot's tunnel —
they're two different local servers on two different ports.
Deploying (production)
Deploy server-http.js the same way as the WhatsApp bot — Railway or Render both work:
Push this repo to GitHub
Connect it in Railway/Render as its own service (separate from the WhatsApp bot service)
Set the same env vars from
.envin their dashboardStart command is
npm run start:http(already configured viarailway.jsonfor Railway)Once deployed, take the resulting URL +
/mcpand put it in the WhatsApp bot's.envasGHL_MCP_URL, with the bot'sJWT_SECRETmatching this service'sJWT_SECRET
Available tools
search_contacts— find a lead/customer by name, email, or phoneget_conversations— list conversations for a contactget_conversation_timeline— full message timeline (SMS/email/calls) for a conversationget_call_transcript— transcript + direction for a specific calllist_brokers— team members and their GHL user IDsget_broker_leads_overview— touch/call counts per lead for a brokerlist_pipelines— pipelines and stages with IDsget_opportunities_by_stage— leads sitting in a specific pipeline stagecreate_task— create a follow-up task (only when explicitly asked)
Security note
server-http.js sits on the public internet in front of real lead, broker, and pipeline
data once deployed. The JWT verification in requireAuth, plus the per-broker ownership
checks in access.js, are the only things standing between that data and anyone who finds
the URL — don't skip setting JWT_SECRET, don't reuse a weak/guessable value, and don't
commit .env (already covered by .gitignore).
This server cannot be deployed
Maintenance
Related MCP Connectors
LeadConnector / GoHighLevel MCP Pack — wraps the GoHighLevel CRM for AI agents.
API-first CRM for LLMs - contacts, companies, deals and activities over a native MCP server.
Marketo MCP server for AI. 130 tools to operate Marketo from Claude, Cursor, or ChatGPT.
A CRM powered by your agent: contacts, deals, email, ads, and reports over MCP.
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