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🧠 GHL MCP Server – Anthropic LangGraph Agent Integration

@JewelreyBoxAI

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About 🧠 GHL MCP Server – Anthropic LangGraph Agent Integration

GHL MCP server to GHL Demo Sub Account

#mathgpt#math-solver#math-assistant

Config

Add this server to your MCP-compatible client using the configuration below.

{
  "mcpServers": {
    "MCP-GHL": {
      "command": "python",
      "args": [
        "scripts/start_server.py"
      ]
    }
  }
}

Tools

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We auto-extract tools from the README. The maintainer can list them under a ## Tools heading to populate this section.

Overview

What is 🧠 GHL MCP Server – Anthropic LangGraph Agent Integration?

This server exposes GoHighLevel (GHL) sub-account tools to an Anthropic-powered LangGraph agent using the Model Context Protocol (MCP). It acts as a secure intermediary between the LangChain ecosystem and GHL APIs, designed for clean modular separation and zero-handoff latency.

How to use 🧠 GHL MCP Server – Anthropic LangGraph Agent Integration?

Create a .env file with your GHL API key, sub-account ID, and base URL. Install dependencies with pip install -r requirements.txt. Start the server using uvicorn mcp_server:app --host 0.0.0.0 --port 8000 or the startup script. Test connection with python examples/simple_client.py or run interactive mode.

Key features of 🧠 GHL MCP Server – Anthropic LangGraph Agent Integration

  • FastAPI MCP server exposing GHL sub-account tools
  • Dynamic tool registration via LangChain MCP adapters
  • Secure credential loading through .env variables
  • Pre-built tools for contacts, pipelines, and automations
  • Supports Claude-based LangGraph Agents as MCP clients
  • Zero-coupling between agent and GHL API

Use cases of 🧠 GHL MCP Server – Anthropic LangGraph Agent Integration

  • A Claude-powered agent retrieves contact details from GHL in real time
  • Automates pipeline and opportunity updates without manual login
  • Triggers custom workflows by calling webhook endpoints
  • Creates notes on contacts based on user conversation context
  • Enables LangGraph agents to manage GHL sub-accounts programmatically

FAQ from 🧠 GHL MCP Server – Anthropic LangGraph Agent Integration

What is the purpose of this server?

It acts as a secure intermediary between LangChain agents and GoHighLevel APIs, exposing GHL tools (contacts, pipelines, automations) to Claude-based LangGraph agents via MCP.

What environment variables are required?

You need GHL_API_BASE_URL, GHL_API_KEY, GHL_SUB_ACCOUNT_ID, and optionally ALLOWED_ORIGINS. All are set in a .env file at the root of ghl_mcp_server/.

Where can I find the GHL credentials?

  • GHL_API_KEY: GHL Settings → API Keys
  • GHL_SUB_ACCOUNT_ID: URL bar while inside the sub-account dashboard
  • Webhook/Funnel/Contact IDs: found in respective page URLs or contact details

What are the runtime dependencies?

Python 3.11+, FastAPI, and the langchain-mcp-adapters package. Optionally NGROK or Azure App Service for public MCP endpoint exposure.

How do tools communicate with the agent?

All tool usage is routed over HTTP via LangChain’s MCP standard. The server exposes endpoints that the MCP client calls using MCPClient.load_tools(). No data leaves the user’s GHL sub-account except through authorized API calls.

Frequently asked questions

What is the purpose of this server?

It acts as a secure intermediary between LangChain agents and GoHighLevel APIs, exposing GHL tools (contacts, pipelines, automations) to Claude-based LangGraph agents via MCP.

What environment variables are required?

You need `GHL_API_BASE_URL`, `GHL_API_KEY`, `GHL_SUB_ACCOUNT_ID`, and optionally `ALLOWED_ORIGINS`. All are set in a `.env` file at the root of `ghl_mcp_server/`.

Where can I find the GHL credentials?

- `GHL_API_KEY`: GHL Settings → API Keys - `GHL_SUB_ACCOUNT_ID`: URL bar while inside the sub-account dashboard - Webhook/Funnel/Contact IDs: found in respective page URLs or contact details

What are the runtime dependencies?

Python 3.11+, FastAPI, and the `langchain-mcp-adapters` package. Optionally NGROK or Azure App Service for public MCP endpoint exposure.

How do tools communicate with the agent?

All tool usage is routed over HTTP via LangChain’s MCP standard. The server exposes endpoints that the MCP client calls using `MCPClient.load_tools()`. No data leaves the user’s GHL sub-account except through authorized API calls.

Comments

Basic information

Category

AI & Agents

Transports

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Author

JewelreyBoxAI

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