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vid-agent-mcp

MCP server for video understanding — transcribe Bilibili videos, search, and analyze local files through natural conversation with Claude.

License

Chat with Claude and ask it to watch a video for you. Send a Bilibili link, get back a structured summary with key points, visual moments, and a "worth watching" verdict — all through MCP tools.

How it works

You: "转录一下这个视频 BV1pzjy6GEkC"

Claude: *calls transcribe tool*
        → Downloads video via BBDown
        → Transcribes audio with faster-whisper
        → Identifies key visual moments via VLM
        → Returns structured summary

You: "最近有什么AI Agent新视频?"

Claude: *calls search_by_intent tool*
        → Expands intent into multiple search queries
        → Searches Bilibili concurrently
        → Merges & ranks results by popularity

Related MCP server: Bilibili API MCP Server

Tools

Tool

Description

transcribe

Download + analyze a Bilibili video

transcribe_local

Analyze a local video file

search

Search Bilibili by keyword

search_by_intent

AI-powered search with natural language

get_result

Read a previously saved result

Setup

Prerequisites

  • Python 3.11+

  • ffmpeg (install via conda install ffmpeg or system package manager)

  • BBDown (for Bilibili downloads) — install from here or use an existing install

  • A MiMo API key (or any OpenAI-compatible VLM API)

Install

# 1. Clone
git clone https://github.com/ikerrrrrrrrrrr/bili-vid-agent
cd bili-vid-agent

# Or install the MCP server directly
pip install vid-agent-mcp

# 2. Configure
cp .env.example .env
# Edit .env with your API key

# 3. Run
vid-agent-mcp

Claude Desktop configuration

Add to your claude_desktop_config.json:

{
  "mcpServers": {
    "vid-agent": {
      "command": "vid-agent-mcp",
      "env": {
        "VLM_API_KEY": "your-key-here"
      }
    }
  }
}

Or point to a local install:

{
  "mcpServers": {
    "vid-agent": {
      "command": "conda",
      "args": ["run", "-n", "vid_agent", "vid-agent-mcp"]
    }
  }
}

Configuration

All config via .env file or environment variables:

Variable

Default

Description

VLM_API_KEY

MiMo API key (required)

VLM_BASE_URL

https://api.xiaomimimo.com/v1

API base URL

VLM_MODEL

mimo-v2-omni

Model for visual analysis

SUMMARY_MODEL

mimo-v2.5-pro

Model for summary generation

BBDOWN_PATH

bbdown

Path to BBDown binary

WHISPER_MODEL

turbo

Whisper model size

CACHE_DIR

./cache

Download/transcription cache

WORK_DIR

./work

Working directory

License

Apache 2.0

A
license - permissive license
-
quality - not tested
C
maintenance

Maintenance

Maintainers
Response time
Release cycle
Releases (12mo)
Commit activity

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