TwitterAPI.io MCP Server

Twitter Data Access Capabilities for AI Agents and AI Coding Assistants

A powerful implementation of the Model Context Protocol (MCP) integrated with TwitterAPI.io for providing AI agents and AI coding assistants with advanced Twitter data access capabilities.

With this MCP server, you can access tweets, user profiles, and search functionality and then use that knowledge anywhere.

Overview

This MCP server provides tools that enable AI agents to access Twitter data through TwitterAPI.io's service, including retrieving tweets, user profiles, followers, and performing searches. It follows the best practices for building MCP servers.

Features

• Tweet Retrieval: Get tweets by ID
• User Profiles: Access user profile information
• Timeline Access: Retrieve a user's recent tweets
• Network Analysis: Get user followers and following
• Search Capabilities: Search tweets with advanced query support
• Replies Retrieval: Get replies to specific tweets

Tools

The server provides six essential Twitter data access tools:

• get_tweet: Get a tweet by its ID
• get_user_profile: Get a Twitter user's profile information
• get_user_recent_tweets: Get a user's recent tweets
• search_tweets: Search for tweets based on a query
• get_user_followers: Get a list of users who follow the specified user
• get_user_following: Get a list of users that the specified user follows

Prerequisites

• Docker/Docker Desktop if running the MCP server as a container (recommended)
• Python 3.10+ if running the MCP server directly
• TwitterAPI.io API key

Installation

Using Docker (Recommended)

1. Clone this repository:

git clone https://github.com/yourusername/twitterapi.io-mcp.git
cd twitterapi.io-mcp

2. Build the Docker image:

docker build -t mcp/twitterapi-io --build-arg PORT=8051 .

3. Create a .env file based on the configuration section below

Using Python directly (no Docker)

1. Clone this repository:

git clone https://github.com/yourusername/twitterapi.io-mcp.git
cd twitterapi.io-mcp

2. Install dependencies:

pip install -e .

3. Create a .env file based on the configuration section below

Configuration

Create a .env file in the project root with the following variables:

# MCP Server Configuration
HOST=0.0.0.0
PORT=8051
TRANSPORT=sse

# TwitterAPI.io Configuration
TWITTER_API_KEY=your_twitterapi_io_key

Running the Server

Using Docker

docker run --env-file .env -p 8051:8051 mcp/twitterapi-io

Using Python

python src/main.py

The server will start and listen on the configured host and port.

Integration with MCP Clients

SSE Configuration

Once you have the server running with SSE transport, you can connect to it using this configuration:

{
  "mcpServers": {
    "twitterapi-mcp": {
      "transport": "sse",
      "url": "http://localhost:8051/sse"
    }
  }
}

Note for Windsurf users: Use serverUrl instead of url in your configuration:

{
  "mcpServers": {
    "twitterapi-mcp": {
      "transport": "sse",
      "serverUrl": "http://localhost:8051/sse"
    }
  }
}

Note for Docker users: Use host.docker.internal instead of localhost if your client is running in a different container.

Stdio Configuration

Add this server to your MCP configuration for Claude Desktop, Windsurf, or any other MCP client:

{
  "mcpServers": {
    "twitterapi-mcp": {
      "command": "python",
      "args": ["path/to/twitterapi-mcp/src/main.py"],
      "env": {
        "TRANSPORT": "stdio",
        "TWITTER_API_KEY": "your_twitterapi_io_key"
      }
    }
  }
}

Docker with Stdio Configuration

{
  "mcpServers": {
    "twitterapi-mcp": {
      "command": "docker",
      "args": ["run", "--rm", "-i", 
               "-e", "TRANSPORT", 
               "-e", "TWITTER_API_KEY",
               "mcp/twitterapi-io"],
      "env": {
        "TRANSPORT": "stdio",
        "TWITTER_API_KEY": "your_twitterapi_io_key"
      }
    }
  }
}

Usage Examples

# Example of using the client with MCP
from mcp.client import Client

async with Client("twitterapi-mcp") as client:
    # Get a user profile
    user = await client.get_user_profile(username="twitterapi")
    print(user)
    
    # Search for tweets
    tweets = await client.search_tweets(query="python", count=5)
    print(tweets)

License

MIT