gnosis

Without a docs server

• LLMs hallucinate API signatures that don't exist
• Entire files dumped into context — 3,000–15,000 tokens per doc
• Architecture decisions buried across dozens of files
• Every repeated lookup pays full context cost

With Gnosis MCP

• search_docs returns ranked, highlighted excerpts — typically 300–800 tokens
• Real answers grounded in your actual docs, not guesses from training data
• One local index across hundreds of files — instant multi-doc search
• 5–10× token savings per lookup when your corpus covers the question

What makes gnosis-mcp different

• Your data stays on your machine. SQLite by default, PostgreSQL at scale — nothing leaves the host.
• Index anything that's docs-shaped. Markdown, git commit history, crawled websites — one index, one search API.
• Measured, not marketed. Ships BEIR SciFact numbers (0.671 nDCG@10 — within 1 % of the Lucene BM25 baseline), a reproducible eval harness (gnosis-mcp eval), and a chunk-size sweep showing where the quality plateau actually sits.

Full side-by-side vs Context7 / docs-mcp-server / mcp-local-rag: gnosismcp.com#compare.

Features

• Zero config — SQLite by default, pip install and go
• Hybrid search — keyword (BM25) + semantic (local ONNX embeddings, no API key). Tune RRF fusion with GNOSIS_MCP_RRF_K.
• Cross-encoder reranking — optional [reranking] extra with a 22M-param ONNX model. Off by default. Test on your own corpus before enabling — the bundled MS-MARCO reranker hurts dev-doc retrieval in our measurements.
• Git history — ingest commit messages as searchable context (ingest-git)
• Web crawl — ingest documentation from any website via sitemap or link crawl
• Multi-format — .md .txt .ipynb .toml .csv .json + optional .rst .pdf
• Auto-linking — relates_to frontmatter creates a navigable document graph
• Watch mode — auto-re-ingest on file changes
• Prune stale docs — gnosis-mcp ingest --prune removes chunks whose source file was deleted. --wipe for a full reset before re-ingest.
• Built-in eval harness — gnosis-mcp eval prints Hit@K / MRR / Precision@K in one command
• PostgreSQL ready — pgvector + tsvector when you need scale

Performance

Fast. 8.7 ms mean MCP round-trip. Hybrid search p50 < 30 ms on a 700-doc corpus. Keyword QPS scales from 9,463 @ 100 docs to 471 @ 10,000 docs (full numbers).

Finds the right answer. On 558 real dev docs with 25 hand-written golden queries: Hit@5 = 0.92, nDCG@10 = 0.87, MRR = 0.79. On BEIR SciFact (5,183 docs, public retrieval benchmark): nDCG@10 = 0.671 — within 1 % of the Lucene BM25 baseline.

Tokens saved. Each search_docs call returns 200–500 tokens of on-point snippets instead of the 3,000–15,000 tokens a full-file Read would have cost. Track your own with gnosis-mcp savings (v0.12.0+) — the ledger writes to search_access_log on every call and aggregates per tool per --days N: