pipecat-docs-mcp

> Search 317 pages of Pipecat docs + 1,028 GitHub Issues from Claude Desktop or Cursor — grounded answers, no hallucinated APIs.

[](LICENSE) [](https://python.org) [](https://github.com/jlowin/fastmcp)

---

Indexes 8,817 chunks from the Pipecat docs and GitHub Issues, then exposes them through 4 MCP tools. Claude can answer questions about configuration, code examples, architecture concepts, and provider comparisons — with source-cited results pulled directly from the official docs and community issue threads.

Retrieval pipeline: BM25 + FAISS dense vectors → Reciprocal Rank Fusion → Cross-encoder reranker

| Metric | Value | |---|---| | Pages indexed | 317 | | GitHub Issues indexed | 1,028 | | Total chunks | 8,817 | | Mean Reciprocal Rank | 0.863 | | Recall@5 | 100% | | Avg query latency | 35ms |

---

| Tool | Use it when... | |---|---| | search_pipecat_docs | General questions, config options, error messages | | get_example_code | You need a runnable Python pipeline | | explain_concept | "What is a Frame / Pipeline / VAD / Transport?" | | compare_services | Choosing between STT, TTS, or transport providers |

---

git clone https://github.com/DTufail/pipecat-docs-mcp.git
cd pipecat-docs-mcp
python -m venv .venv && source .venv/bin/activate
pip install -r requirements.txt

data/chunks.jsonl is included. This builds the BM25 and FAISS indexes (~2 min):

python indexer.py

Adds 1,028 issues from pipecat-ai/pipecat for error-driven retrieval:

export GITHUB_TOKEN="your_token_here"   # optional but recommended (5k req/hr vs 60)
python github_in