BiliStalkerMCP

Bilibili MCP Server for Specific User Analysis

BiliStalkerMCP is a Bilibili MCP server built on Model Context Protocol (MCP), designed for AI agents that need to analyze a specific Bilibili user or creator.

It is optimized for workflows that start from a target uid or username, then retrieve that user's profile, videos, dynamics, articles, subtitles, and followings with structured tools.

If you are searching for a Bilibili MCP server, a Bilibili Model Context Protocol server, or an MCP server for tracking and analyzing a specific Bilibili user, this repository is designed for that use case.

English | 中文说明

Installation

uvx bili-stalker-mcp
# or
pip install bili-stalker-mcp

Configuration (Claude Desktop, Recommended)

{
  "mcpServers": {
    "bilistalker": {
      "command": "uv",
      "args": ["run", "--directory", "/path/to/BiliStalkerMCP", "bili-stalker-mcp"],
      "env": {
        "SESSDATA": "required_sessdata",
        "BILI_JCT": "optional_jct",
        "BUVID3": "optional_buvid3"
      }
    }
  }
}

Prefer uv run --directory ... for faster local updates when PyPI release propagation is delayed. You can still use uvx bili-stalker-mcp for quick one-off usage.

Auth: Obtain SESSDATA from Browser DevTools (F12) > Application > Cookies > .bilibili.com.

Environment Variables

Available Tools

Dynamic Filtering (dynamic_type)

• ALL (default): Text, Draw, and Reposts.
• ALL_RAW: Unfiltered (includes Videos & Articles).
• VIDEO, ARTICLE, DRAW, TEXT: Specific category filtering.

Pagination: Responses include next_cursor. Pass this to subsequent requests for seamless scrolling.

Subtitle Modes (get_video_detail)

• smart (default when fetch_subtitles=true): fetch metadata for all pages, download only one best-matched subtitle track text.
• full: download text for all subtitle tracks (higher cost).
• minimal: skip subtitle metadata and subtitle text fetching.

subtitle_lang can force a language (for example en-US); auto uses built-in priority fallback.
subtitle_max_chars caps returned subtitle text size to avoid token explosion.

Bundled Skill

The repository ships a ready-to-use AI agent skill in skills/bili-content-analysis/:

skills/bili-content-analysis/
├── SKILL.md                        # Workflow & output contract
└── references/
    └── analysis-style.md           # Detailed writing style rules

What It Does

Guides compatible AI agents (Gemini, Claude, etc.) through a structured 6-step workflow for deep Bilibili content analysis:

1. Clarify target and scope (uid / bvid / keyword).
2. Collect evidence — lightweight lists first, heavy detail only for high-value items.
3. Reconstruct source structure before interpreting (timeline, chapters, speakers).
4. Analyze — facts, logic chain, assumptions, themes, and shifts.
5. Retain anchors — uid, bvid, article_id, timestamps, key source snippets.
6. Handle failures — state blockers explicitly, stop speculation.

Usage

Copy the bili-content-analysis folder into your project's skill directory:

The agent will automatically activate the skill when user requests involve Bilibili creator tracking, transcript interpretation, timeline reconstruction, or content analysis.

Development

# Setup
git clone https://github.com/222wcnm/BiliStalkerMCP.git
cd BiliStalkerMCP
uv pip install -e .[dev]

# Test
uv run pytest -q

# Integration & Performance (Requires Auth)
uv run python scripts/integration_suite.py -u <UID>
uv run python scripts/perf_baseline.py -u <UID> --tools dynamics -n 3

Release (Maintainers)

Prerequisite: Ensure that a .pypirc file is configured in your user home directory to provide PyPI credentials.

# Build + test + twine check (no upload)
.\scripts\pypi_release.ps1

# Upload to TestPyPI
.\scripts\pypi_release.ps1 -TestPyPI -Upload

# Upload to PyPI
.\scripts\pypi_release.ps1 -Upload

Docker

Runs via stdio transport. No ports exposed.

docker build -t bilistalker-mcp .
docker run -e SESSDATA=... bilistalker-mcp

Troubleshooting

• 412 Precondition Failed: Bilibili anti-crawling system triggered. Refresh SESSDATA or provide BUVID3.
• Cloud IPs: Highly susceptible to blocking; local execution is recommended.

License

MIT

Disclaimer: For personal research and learning only. Bulk profiling, harassment, or commercial surveillance is prohibited.

This project is built and maintained with the help of AI.