Bilibili MCP Server

The Bilibili MCP Server bridges the rich media ecosystem of bilibili.com with AI assistants that speak the Model Context Protocol. By exposing a set of declarative resources, it lets developers query user profiles, retrieve video metadata, and search for content using familiar identifiers such as (user ID) or (video ID). This eliminates the need to write custom HTTP clients or handle authentication flows for bilibili’s public API, enabling AI assistants to deliver instant, context‑aware responses about popular videos, creators, or trending topics.

For developers building AI‑powered workflows, the server provides a clean, high‑level interface that integrates seamlessly into existing MCP setups. A single configuration line in the section launches a lightweight Node.js process that listens for resource requests. The server’s built‑in tools translate those requests into the appropriate bilibili API calls, parse the JSON responses, and return them in a format that Claude or other MCP clients can consume directly. This abstraction saves time, reduces boilerplate, and ensures that downstream AI models receive consistent, well‑structured data.

Key capabilities include:

• User information lookup: Retrieve public profile details by user ID, allowing assistants to reference creators or analyze audience demographics.
• Video metadata retrieval: Fetch comprehensive details for a specific video using its , enabling content summaries, title extraction, or embedding recommendations.
• Keyword search: Query the platform’s search endpoint to surface videos that match user‑provided keywords, supporting dynamic content discovery within conversations.

These features empower a range of real‑world scenarios. A virtual tour guide could ask about the latest music videos from a particular artist, and the assistant would pull the exact title and description. An educational chatbot might recommend tutorial videos on a technical topic, pulling metadata to generate concise previews. Marketing teams could automate sentiment analysis by fetching trending videos and extracting engagement metrics.

The server’s integration model is straightforward: after installation, the MCP client registers the server. When a user asks a question that requires bilibili data, the assistant issues an call to the server’s resources. The server handles rate limiting, error handling, and data normalization behind the scenes, returning a clean payload ready for natural language generation. This tight coupling between AI and external data sources reduces latency, improves reliability, and keeps the developer focused on crafting conversational logic rather than API plumbing.

In summary, the Bilibili MCP Server transforms bilibili’s vast video catalog into an AI‑ready data source. By abstracting API complexity and offering ready‑to‑use resources, it enables developers to enrich AI assistants with dynamic media content, streamline workflows, and deliver engaging, data‑driven interactions.