What does this server do?
It provides an MCP interface to the Bilibili API, allowing MCP clients to search videos, find users, retrieve user content (videos, posts, collections), and fetch video danmaku programmatically.
@SMYB5431
Basic information
No overview available yet
Add this server to your MCP-compatible client using the configuration below.
{
"mcpServers": {
"bilibili": {
"type": "stdio",
"isActive": true,
"command": "uvx",
"args": [
"bilibili-api-mcp-server"
]
}
}
}Tool list not available yet
This listing does not currently have a tool list to display. This does not mean the server has no tools; check its documentation for supported capabilities.
Fetch the live tool list by running this server in a temporary sandbox using the button above.
An MCP (Model Context Protocol) server that wraps the Bilibili API, enabling AI assistants to search for videos and users, retrieve user content, and fetch video danmaku (bullet comments). It is intended for developers integrating Bilibili data into MCP-compatible clients.
Use uvx bilibili-api-mcp-server as the command in any MCP client’s JSON configuration. Alternatively, clone the repository, install dependencies with uv sync, and point the client to the local installation. The server exposes multiple tools for searching, user content retrieval, and danmaku fetching.
It provides an MCP interface to the Bilibili API, allowing MCP clients to search videos, find users, retrieve user content (videos, posts, collections), and fetch video danmaku programmatically.
Python with `uv` (a fast project manager). The server can be run via `uvx` from PyPI without manual installation, or cloned and run locally with `uv sync`.
Add a JSON entry under `mcpServers` with command `uvx` and args `["bilibili-api-mcp-server"]`. For local development, use `uvx` with `--directory` pointing to the cloned repository.
It accesses publicly available Bilibili data: video search results, user profiles, user dynamics (posts), video lists, collection metadata, and danmaku. No authentication or private data is required.
The README does not specify API rate limits or authentication requirements; it only mentions configurable result counts (e.g., top 15 videos by default).

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