Local stdio MCP server that turns folders of dense documents into a vector embedding DB Claude can search.
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# vectorise-mcp Local stdio MCP server that turns folders of dense documents (PDFs, Word, text, markdown) into a **hybrid retrieval database** that **Claude Desktop** can semantically search mid-conversation. Built so Claude can effectively work with corpora far larger than its context window — point it at 100M+ tokens of reports and ask questions; it pulls only the *relevant* chunks. Fully offline after first model download. No API keys. Free. ## Why this is built for *quality*, not just for ticking a box Cheap RAG implementations ("just embed with MiniLM and dot-product search") fail badly on dense documents. They miss rare terms, conflate similar sentences, and rank irrelevant chunks at the top. This server uses a stack designed for real retrieval quality: | Stage | What it does | Why | |---|---|---| | **BGE-small-en-v1.5** embeddings | Dense semantic vectors (384-dim, normalized) | Top-tier MTEB scores at small size; far better than MiniLM on technical English. | | **SQLite FTS5 BM25** | Keyword retrieval in parallel | Catches rare terms, names, IDs, acronyms that pure semantic search misses. | | **Reciprocal Rank Fusion** | Merges vector + keyword candidates | Robust hybrid signal — neither side dominates. | | **bge-reranker-base** cross-encoder | Re-scores top-50 jointly with the query | Massive precision boost; cross-encoders consistently rank 5-10 points higher than bi-encoders alone. | | **Sentence-aware chunking** | 384-tok chunks, 96-tok overlap, sentence-bounded | Preserves coherence; overlap stops boundary loss. | | **SHA1 incremental reindex** | Only re-embeds changed files | Cheap to keep up to date as the folder evolves. | This is the same retrieval pattern used in production search systems (e.g., Anthropic's contextual retrieval, Vespa hybrid recipes). ## Stack | Component | Library | |---|---| | MCP server | [`mcp` SDK](https://github.com/modelcontextprotocol/python-sdk) (FastMCP) | | Embeddings | [`BAAI/bge-small-en-v1.5`](https://hugging…
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