Bilibili Follower Count MCP Server

Overview

The Bilibili MCP Server is a lightweight example of how to expose an external web service as an AI‑assistant capability using the Model Context Protocol. It demonstrates how to package a simple search tool that queries Bilibili’s public API and returns results in a format that Claude or any MCP‑compatible client can consume. By turning Bilibili’s vast video and live‑stream catalog into a first‑class tool, developers can enrich conversational agents with up‑to‑date media content without building custom scrapers or handling authentication themselves.

What problem does it solve?
Many conversational agents need to surface real‑time information from specialized domains, but the typical approach—hardcoding HTTP requests or embedding large datasets—is brittle and hard to maintain. The Bilibili MCP Server abstracts the complexities of network communication, pagination, rate‑limiting, and data normalization behind a clean MCP interface. Developers can focus on higher‑level logic (e.g., recommending videos, summarizing trending topics) while the server handles all interactions with Bilibili’s endpoints.

Key features and capabilities

• General Search Tool () – Accepts a keyword string, forwards the query to Bilibili’s search API, and returns a structured list of results (title, URL, uploader, view count).
• StdIO Mode – The server runs in standard‑input/output mode, making it easy to deploy as a local process or within containerized environments without additional networking configuration.
• MCP‑ready – Implements the full MCP handshake, resource declaration, and tool invocation flow so any compliant client can discover and call seamlessly.
• Extensible Skeleton – The codebase is intentionally minimal, providing a clear template for adding more Bilibili‑specific tools (e.g., live stream status, user statistics) or integrating other APIs.

Use cases and real‑world scenarios

• Content Discovery – A chatbot that helps users find Bilibili videos matching their interests, returning clickable links directly in the conversation.
• Trend Monitoring – An internal tool that pulls daily search results to surface emerging topics for marketing teams.
• Education & Research – Researchers can programmatically query Bilibili to gather datasets for media analysis, all orchestrated through an MCP client.
• Multimodal Assistants – A voice‑enabled assistant can fetch the latest Bilibili clips and play them on demand, leveraging the server’s simple interface.

Integration with AI workflows
Once deployed, any MCP‑compatible client can list available tools and invoke with a natural language query. The server returns JSON‑encoded results, which the assistant can then format into rich messages or trigger downstream actions (e.g., opening a video in a browser). Because the server runs locally, latency is low and privacy concerns are mitigated—ideal for internal applications or offline environments.

Unique advantages

• Zero‑code client integration – Developers need only point their MCP client to the server’s stdio endpoint; no custom adapters are required.
• Domain‑specific expertise – By encapsulating Bilibili’s API quirks, the server delivers a consistent, predictable interface that abstracts away version changes or pagination logic.
• Rapid prototyping – The minimal example serves as a springboard for building more sophisticated media‑centric assistants without reinventing the MCP plumbing.

In summary, the Bilibili MCP Server turns a popular video platform into an AI‑friendly toolset, enabling developers to enrich conversational experiences with real‑time content discovery while keeping the integration surface clean and protocol‑standardized.