🐰 Membit MCP Server

Membit MCP Server connects your AI systems to live social insights through Membit's API. By leveraging the Model Context Protocol (MCP), this server makes real-time social data—from trending discussion clusters to raw posts—readily available to your AI applications. Whether you're using Claude Desktop, Goose, Cursor, or any MCP-compatible client, this server enriches your model’s context with current social data.

Contents

• Introduction
• Key Features
• Requirements
• Installation Guide
  • PyPI Package
  • Clone from Git
• Client Configuration
  • Using Cursor
  • Using Goose
  • Using Claude Desktop
• How It Works
• Example Interactions
• FAQ & Troubleshooting
• Credits
• License

Introduction

Membit MCP Server is designed to empower AI agents with timely social context. It translates Membit's REST endpoints into MCP tools, allowing your AI to:

• 🔍 Discover Trending Discussions: Search for and retrieve clusters of social discussions.
• 🕳️ Dive Deeper: Fetch detailed posts and metadata for specific clusters.
• 🧠 Extract Insights: Look up raw posts related to any keyword.

By integrating these capabilities, your AI can better understand and respond to rapidly evolving social narratives.

Key Features

• ⚡️ Real-Time Data: Pull live data from Membit's continuously updated API.
• 🔌 MCP Compatibility: Seamlessly integrates with any client that supports the Model Context Protocol.
• 🚀 Simplicity & Flexibility: A lightweight Python server that uses FastMCP for rapid development.

Requirements

Make sure your system includes:

• A valid Membit API key (get one by registering on Membit)
• Python 3.10 or later (check with python --version)
• An MCP-capable client (e.g., Claude Desktop or Goose)
• Git (if you plan to clone the repository)

Use Without Installation

Make sure you have uv installed:

uvx membit-mcp

Installation Guide

PyPI Package

pip install membit-mcp

Clone from Git

Alternatively, to work from the source:

1. Clone the repository:

   git clone https://github.com/membit-ai/membit-mcp.git
   cd membit-mcp
   

2. Install the required dependencies:

   pip install -r requirements.txt
   

3. Launch the server:

   python membit_mcp.py
   

Client Configuration

Using Claude Desktop

For Claude Desktop, create or modify the configuration file:

• macOS: $HOME/Library/Application Support/Claude/claude_desktop_config.json
• Windows: %APPDATA%\Claude\claude_desktop_config.json

Insert the following JSON snippet (substitute your API key):

{
  "mcpServers": {
    "membit-mcp": {
      "command": "uvx",
      "args": ["membit-mcp"],
      "env": {
        "MEMBIT_API_KEY": "your-api-key"
      }
    }
  }
}

Using Goose

Goose is a lightweight MCP client that can integrate directly with your MCP servers. To use Membit MCP with Goose:

1. Install Goose: Follow the installation instructions on the Goose website.
2. Add Membit MCP Extension in Goose:
   • Copy the following address and paste it in your browser (it'll open Goose automatically):

     goose://extension?cmd=uvx&arg=membit-mcp&id=membit&name=Membit%20Real-time%20Data&description=Real-time%20social%20posts%20and%20cluster%20capabilities%20powered%20by%20Membit&env=MEMBIT_API_KEY%3DAPI%20key%20for%20Membit%20real-time%20data%20service
     

   • Add your Membit API key in the environment variable.
3. Using the Tools:
   Once configured, Goose will display the tools:
   • membit-clusters-search
   • membit-clusters-info
   • membit-posts-search
     You can now incorporate these tools into your agent workflows within Goose to fetch live social data.

Using Cursor

1. Open Cursor’s settings.
2. Navigate to Features > MCP Servers.
3. Click "+ Add New MCP Server".
4. Enter:
   • Name: membit-mcp
   • Type: command
   • Command:

     env MEMBIT_API_KEY=your-api-key uvx membit-mcp
     

     Replace your-api-key with your actual Membit API key.

How It Works

The server uses the Model Context Protocol to expose three primary tools:

• membit-clusters-search:
  Sends a GET request to https://api-app.membit.ai/clusters/search with a query string and limit.
• membit-clusters-info:
  Fetches details from https://api-app.membit.ai/clusters/info using a cluster label.
• membit-posts-search:
  Searches for raw social posts by keyword via https://api-app.membit.ai/posts/search.

Each tool is accessible via MCP, and responses are formatted as human-readable JSON for easy integration.

FAQ & Troubleshooting

Q: The server doesn’t start. What should I do?
A: Ensure you have Python 3.10+ installed and that the MEMBIT_API_KEY is correctly set in your environment.

Q: I don’t see the tools in my MCP client.
A: Try refreshing the server list. Also, check your server logs for any initialization errors.

Q: I’m receiving API errors from Membit.
A: Verify your Membit API key and confirm that your API usage hasn’t exceeded any limits.

Credits

• Membit: For their robust social data API.
• Model Context Protocol: For the standardized framework that makes seamless integration possible.
• Special thanks to the open-source community for their continuous improvements.

Development Guide

Make sure you have uv installed:

uv run poetry install

To run development server, use the following command:

uv run mcp dev membit_mcp.py

To build and publish to PyPI:

uv run poetry build
uv run poetry publish

License

This project is distributed under the MIT License.