Misskey MCP Server

The Misskey MCP Server fills a niche that many AI‑assistant developers have been waiting for: the ability to let Claude or other Model Context Protocol clients interact directly with a Misskey instance. Instead of wrapping the REST API in custom code for every request, this server exposes a set of MCP tools that translate a simple JSON payload into an authenticated Misskey call. The result is a clean, declarative interface that lets AI assistants ask for data or perform actions—such as posting notes, retrieving a user’s timeline, or fetching recent notifications—without leaking API secrets into the prompt.

At its core, the server implements a minimal but functional subset of Misskey’s endpoints. The only fully supported tool right now is post_misskey_note, which creates a new note on the instance. Other tools are listed in the feature matrix and are marked “pending,” giving developers a clear roadmap for future contributions. Each tool is mapped to the corresponding Misskey API endpoint, and the server takes care of authentication using a bearer token supplied via environment variables. This means developers can focus on the logic of their AI workflow rather than handling OAuth or token rotation.

The value for developers is twofold. First, it removes the need to write boilerplate code that handles HTTP requests, JSON serialization, and error handling for every Misskey operation. Second, it integrates seamlessly into existing MCP‑enabled AI pipelines: a prompt can simply call with the desired content, and the assistant will publish it to a user’s Misskey account. This is particularly useful for bots that generate social media posts, content‑moderation tools that flag and react to user activity, or even automated support agents that log tickets as Misskey notes.

Typical use cases include:

• Social media automation – A Claude agent can schedule posts, respond to mentions, or create threaded conversations by invoking the appropriate MCP tools.
• Federated collaboration – Teams can use AI to draft updates that are automatically shared across a Misskey federation, keeping everyone in sync.
• Data ingestion – An AI assistant can pull user or timeline data (once the corresponding tools are implemented) and feed it into downstream analytics pipelines.

Because the server is written in Go and follows the MCP specification closely, it can be deployed behind a reverse proxy or run locally for testing. Its design encourages community contributions: the feature table clearly indicates which endpoints are ready and which still need implementation, making it straightforward for contributors to add new tools or improve existing ones. In short, the Misskey MCP Server is a lightweight bridge that empowers AI assistants to interact with Misskey in a secure, standardized way, opening the door to rich federated social experiences powered by advanced language models.