HagaiHen/facebook-mcp-server

The Facebook MCP Server bridges the gap between conversational AI agents and Facebook’s Graph API, turning complex REST calls into natural‑language‑friendly functions. By exposing a curated set of tools—such as posting content, moderating comments, and harvesting engagement metrics—the server lets agents like Claude perform real‑world social media tasks without writing code. This abstraction is particularly valuable for teams that need to automate routine interactions on a Facebook Page while retaining the flexibility of LLM‑driven workflows.

For developers, the server offers a straightforward interface: each tool maps directly to a Graph API endpoint, but with parameters that can be supplied by the assistant in plain English. For example, an agent can ask to “post a new update about our product launch” and the server will translate that into an authenticated POST request. The same pattern applies to moderation—agents can delete or hide comments, reply with context‑aware messages, and even filter out negative sentiment before it reaches the public feed. These capabilities reduce manual oversight, improve response times, and help maintain a positive brand voice.

Key features include:

• Content creation: , , and let agents publish text or media, even on a future date.
• Engagement analytics: Tools such as , , and provide quick access to performance metrics that would otherwise require navigating the Facebook dashboard.
• Moderation utilities: , , and empower agents to keep conversations healthy, while ensures timely engagement.
• Direct communication: With , agents can initiate private conversations, opening avenues for personalized outreach.

Real‑world scenarios range from a social media manager using an LLM to automatically post weekly updates and flag negative feedback, to a customer support team that replies to comments through an AI‑driven chatbot. In both cases, the MCP server removes the need for developers to write boilerplate Graph API code, allowing them to focus on higher‑level logic and user experience.

Because the server is fully compliant with MCP standards, it plugs seamlessly into any LLM client that supports the protocol. Developers can chain these tools with other MCP services, creating end‑to‑end workflows that span data ingestion, natural language understanding, and automated action. The result is a powerful, developer‑friendly bridge that turns Facebook’s rich API into an intuitive extension of conversational AI.