A Model Context Protocol (MCP) server implementation that provides seamless integration with Twitter/X API, allowing AI models to interact with Twitter functionalities through a standardized interface.

• Tweet Management

  • Create and post tweets
  • Reply to existing tweets
  • Like and retweet functionality
  • Thread viewing capabilities

• Data Retrieval

  • Fetch tweet details
  • Search functionality
  • Thread reconstruction
  • Metrics tracking (likes, retweets)

• Security

  • Environment-based configuration
  • Secure credential management
  • Error handling and validation

• Python 3.10 or higher
• MCP CLI (mcp[cli])
• Twitter Developer Account with API credentials
• Virtual environment management tool (venv)
• Tweepy library

1. Clone the repository

   git clone https://your-repository-url.git
   cd twitter-mcp-server

2. Create and activate virtual environment

   python3.10 -m venv .venv
   source .venv/bin/activate

3. Install dependencies

   pip install "mcp[cli]" tweepy python-dotenv

4. Configure environment variables Create a .env file in the project root:

   TWITTER_API_KEY=your_api_key_here
   TWITTER_API_SECRET=your_api_secret_here
   TWITTER_ACCESS_TOKEN=your_access_token_here
   TWITTER_ACCESS_TOKEN_SECRET=your_access_token_secret_here

5. Install the MCP server

   mcp install main.py

Create or update your MCP configuration file:

For Claude Desktop (~/Library/Application Support/Claude/claude_desktop_config.json):

{
  "mcpServers": {
    "twitter": {
      "command": "python",
      "args": ["path/to/main.py"],
      "env": {
        "PYTHONPATH": "path/to/project"
      }
    }
  }
}

post_tweet(content: str) -> Dict

Posts a new tweet to your account.

• Parameters:
  • content: Tweet text (max 280 characters)
• Returns:
  • Success response with tweet ID and content
  • Error response if validation fails

reply_to_tweet(tweet_id: str, content: str) -> Dict

Creates a reply to an existing tweet.

• Parameters:
  • tweet_id: ID of the tweet to reply to
  • content: Reply text (max 280 characters)

get_tweet(tweet_id: str) -> Dict

Retrieves details of a specific tweet.

• Parameters:
  • tweet_id: ID of the tweet to fetch
• Returns:
  • Tweet details including metrics

like_tweet(tweet_id: str) -> Dict

Likes a specific tweet.

• Parameters:
  • tweet_id: ID of the tweet to like

retweet(tweet_id: str) -> Dict

Retweets a specific tweet.

• Parameters:
  • tweet_id: ID of the tweet to retweet

get_tweet_thread(tweet_id: str) -> dict

Retrieves a complete thread starting from a tweet.

• Parameters:
  • tweet_id: ID of the thread's root tweet

search_tweets(query: str) -> List[dict]

Searches for tweets containing specific text.

• Parameters:
  • query: Search query string

response = post_tweet("Hello from Twitter MCP Server!")
print(response)

response = reply_to_tweet("1234567890", "This is a reply!")
print(response)

thread = get_tweet_thread("1234567890")
print(thread)

• Never commit .env file to version control
• Regularly rotate API credentials
• Monitor API usage and rate limits
• Validate input data before making API calls

1. Check MCP Server Logs

   tail -n 20 -F ~/Library/Logs/Claude/mcp*.log

2. Enable Developer Tools

   echo '{"allowDevTools": true}' > ~/Library/Application\ Support/Claude/developer_settings.json

3. Test Server Connection

   mcp dev main.py

Twitter API has rate limits that vary by endpoint:

• Tweet creation: 200 per 15 minutes
• Likes: 1000 per 24 hours
• Retweets: 1000 per 24 hours

1. Fork the repository
2. Create a feature branch
3. Commit your changes
4. Push to the branch
5. Create a Pull Request

This project is licensed under the MIT License - see the LICENSE file for details.

• Twitter API Documentation
• MCP Protocol Specification
• Tweepy Library Documentation
• Claude Desktop Development Team

For support, please:

1. Check the documentation
2. Review existing issues
3. Create a new issue with detailed information

• 1.0.0
  • Initial release
  • Basic Twitter functionality
  • MCP integration

• Media attachment support
• Direct message functionality
• Advanced search options
• Analytics integration
• Batch operations
• Rate limit handling

• Keep API credentials secure
• Monitor rate limits
• Test thoroughly before production use
• Keep dependencies updated