MCP server that fetches YouTube transcripts for LLM chat apps.
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# youtube-transcript-mcp Transcribe YouTube videos for LLM chat apps via [MCP](https://modelcontextprotocol.io/). Example prompt: `Summarize https://www.youtube.com/watch?v=uB9yZenVLzg` ## Install Requires [uv](https://docs.astral.sh/uv/getting-started/installation/). Add this to your MCP client's config — works for any MCP-compatible app (Claude Desktop, Claude Code, Cursor, Windsurf, VS Code, Zed, …): ```json { "mcpServers": { "youtube-transcript": { "command": "uvx", "args": ["youtube-transcript-mcp-server"] } } } ``` Equivalent one-liner if your client wants a single command: ```bash uvx youtube-transcript-mcp-server ``` That's it — `uvx` fetches the package from PyPI on first run and caches it. Restart the client after editing its config. ## Tool - `transcribe(youtube_video_url: str) -> str` — fetches the transcript (en/de/es/fr/ru) and prepends an ad-removal instruction for the LLM. ## Develop locally ```bash git clone https://github.com/SeanPedersen/youtube-transcript-mcp cd youtube-transcript-mcp uv venv && uv pip install -r pyproject.toml && source .venv/bin/activate python mcp_server.py ``` Point your MCP client at the local checkout instead of PyPI: ```json { "command": "uv", "args": [ "run", "--with", "fastmcp", "--with", "youtube-transcript-api", "fastmcp", "run", "/absolute/path/to/youtube-transcript-mcp/mcp_server.py" ] } ``` ## Release 1. Bump `version` in `pyproject.toml`. 2. Copy `.env.example` to `.env` and add your [PyPI token](https://pypi.org/manage/account/token/). 3. `./scripts/release.sh` — builds and publishes to PyPI.
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