Bluesky MCP Server

Overview

The Bluesky MCP Server bridges the gap between AI assistants and the ATProtocol, allowing developers to pull real‑time social media context directly into their LLM applications. By exposing a suite of well‑documented tools, the server lets Claude or other MCP‑compatible assistants read, analyze, and interact with Bluesky content as if it were a native data source. This eliminates the need for custom API wrappers or manual authentication flows, enabling rapid prototyping of conversational agents that can browse feeds, search posts, and even publish content on behalf of a user.

At its core, the server provides a set of declarative tools that map to common Bluesky endpoints. Developers can request a user’s home timeline, fetch pinned feeds, or retrieve followers with simple function calls. The server also offers higher‑level search capabilities—such as querying posts by hashtag or searching for people and feeds—and action tools like posting, liking, or following. Each tool is designed to return structured JSON, making it easy for an LLM to ingest and reason over the data without additional parsing logic.

Key features include:

• Contextual Retrieval: Pull posts, threads, and profiles by time range or keyword to enrich the assistant’s knowledge base.
• Analysis & Reporting: Generate summaries, trend reports, and user‑interest profiles from collected data.
• Actionable Interaction: Create posts, like content, and follow users directly from natural language prompts.
• Convenient URL Handling: Convert web URLs to AT URIs, ensuring compatibility across tools that require the native format.

Real‑world scenarios span content creation, social listening, and personal analytics. For instance, a marketing team could ask the assistant to “summarize the latest conversations about our new product launch” and then automatically post a follow‑up announcement. A personal assistant could analyze a user’s liked posts to suggest new topics of interest or generate a weekly digest of their feed. Because the server adheres strictly to MCP, any LLM that understands the protocol can integrate these capabilities with minimal overhead.

Unique advantages of this MCP server are its tight integration with the native ATProtocol and its focus on natural‑language operability. Developers can treat Bluesky interactions as first‑class data sources, just like databases or file systems, and leverage the full power of LLM reasoning to build sophisticated, conversational social media tools without writing bespoke API code.