X MCP Server

The X MCP Server bridges AI assistants—such as Claude, Cursor AI, and Windsurf AI—to the X platform (formerly known as Twitter) through the Model Context Protocol. By exposing a set of MCP endpoints, it lets developers query tweets, post updates, and manage user interactions directly from within their AI workflows without writing custom API wrappers. This eliminates the need to handle OAuth flows, rate‑limit monitoring, and endpoint discovery manually, allowing teams to focus on higher‑level application logic.

At its core, the server authenticates against X using four tokens: an API key, a secret, an access token, and an access token secret. Once configured, any MCP‑enabled client can invoke the server’s resources to perform common X actions such as retrieving timelines, searching for hashtags, or publishing new tweets. The server automatically respects X’s rate‑limit constraints, providing clear error messages when limits are reached and allowing clients to implement back‑off strategies. This built‑in compliance is especially valuable for production deployments where exceeding limits can result in temporary bans.

Key capabilities include:

• Unified resource discovery – Clients receive a catalog of available endpoints, each with clear descriptions and required parameters.
• Secure token management – Credentials are supplied through environment variables, keeping secrets out of code repositories.
• Rate‑limit awareness – The server tracks X’s request quotas and surfaces limit information to the client, enabling graceful degradation.
• Cross‑platform compatibility – Any MCP client that follows the standard can connect, making it a versatile addition to existing AI toolchains.

Typical use cases span marketing automation, social listening, and content moderation. For example, a data‑science team can ask an AI assistant to “list the top 10 tweets mentioning brand X in the last 24 hours” and receive a structured response ready for analysis. A product manager might instruct the assistant to “post an update announcing feature Y” and rely on the server to handle authentication and payload formatting. In both scenarios, the MCP abstraction removes boilerplate code and reduces friction between AI agents and X’s REST API.

Because it follows the MCP specification, developers can integrate this server into any workflow that already supports the protocol—whether it’s a desktop assistant, a web‑based chatbot, or an internal automation pipeline. The result is a plug‑and‑play bridge that turns generic AI commands into concrete X platform actions, streamlining development and accelerating time to value.