gnosis-mcp

Ingest docs → Search with highlights → Stats overview → Serve to AI agents

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:

$ gnosis-mcp savings --days 7
  Tool calls:               142
  Tokens returned:        7,104
  Tokens baseline:      231,580
  Tokens saved:         224,476
  Ratio:                   32.6×

Typical compression runs 10–60× depending on corpus coverage and query specificity — verify on yours. access_log is on by default; GNOSIS_MCP_ACCESS_LOG=false opts out.

Reproducible. gnosis-mcp eval runs a RAG eval harness locally in one second. tests/bench/*.py reproduce every number. Methodology: docs/benchmarks.md.

Rerankers stay off by default. The bundled MS-MARCO cross-encoder drops nDCG@10 by 27 points on dev-docs and adds 400× latency; BGE-reranker-v2-m3 drops it 31 points at 2400×. Test on your corpus before enabling — full write-up: bench-experiments-2026-04-18.

Quick Start

pip install gnosis-mcp           # or: uv tool install gnosis-mcp
gnosis-mcp ingest ./docs/        # loads docs into SQLite (auto-created)
gnosis-mcp serve                 # starts MCP server

That's it. Your AI agent can now search your docs.

Connect your editor — see llms-install.md for copy-paste JSON snippets for Claude Code, Claude Desktop, Cursor, Windsurf, VS Code, JetBrains, and Cline.

Re-organized your docs? gnosis-mcp ingest ./docs --prune re-ingests and removes any DB chunk whose source file no longer exists. --wipe resets the entire index first. Or run gnosis-mcp prune ./docs --dry-run to preview what would be deleted.

Want semantic search? Add local embeddings — no API key needed:

pip install gnosis-mcp[embeddings]
gnosis-mcp ingest ./docs/ --embed   # ingest + embed in one step
gnosis-mcp serve                    # hybrid search auto-activated

Test it before connecting to an editor:

gnosis-mcp search "getting started"           # keyword search
gnosis-mcp search "how does auth work" --embed # hybrid semantic+keyword
gnosis-mcp stats                               # see what was indexed

Multi-arch image, ~140 MB, ships with local ONNX embeddings + REST:

# Serve your ./docs on http://localhost:8000 — MCP at /mcp, REST at /api/*
docker run -p 8000:8000 \
  -v "$PWD/docs:/docs:ro" -v gnosis-data:/data \
  ghcr.io/nicholasglazer/gnosis-mcp:latest

# First-run: ingest into the persistent volume
docker run --rm \
  -v "$PWD/docs:/docs:ro" -v gnosis-data:/data \
  ghcr.io/nicholasglazer/gnosis-mcp:latest \
  ingest /docs --embed

Or use the committed docker-compose.yaml:

docker compose up -d
docker compose exec gnosis gnosis-mcp ingest /docs --embed

Images tagged :latest, :<version>, :<version-minor>, :main, :sha-<sha>.

uvx gnosis-mcp ingest ./docs/
uvx gnosis-mcp serve

Web Crawl

Ingest docs from any website — no local files needed:

pip install gnosis-mcp[web]

# Crawl via sitemap (best for large doc sites)
gnosis-mcp crawl https://docs.stripe.com/ --sitemap

# Depth-limited link crawl with URL filter
gnosis-mcp crawl https://fastapi.tiangolo.com/ --depth 2 --include "/tutorial/*"

# Preview what would be crawled
gnosis-mcp crawl https://docs.python.org/ --dry-run

# Force re-crawl + embed for semantic search
gnosis-mcp crawl https://docs.sveltekit.dev/ --sitemap --force --embed

Respects robots.txt, caches with ETag/Last-Modified for incremental re-crawl, and rate-limits requests (5 concurrent, 0.2s delay). Crawled pages use the URL as the document path and hostname as the category — searchable like any other doc.

Git History

Turn commit messages into searchable context — your agent learns why things were built, not just what exists:

gnosis-mcp ingest-git .                                  # current repo, all files
gnosis-mcp ingest-git /path/to/repo --since 6m           # last 6 months only
gnosis-mcp ingest-git . --include "src/*" --max-commits 5 # filtered + limited
gnosis-mcp ingest-git . --dry-run                         # preview without ingesting
gnosis-mcp ingest-git . --embed                           # embed for semantic search

Each file's commit history becomes a searchable markdown document stored as git-history/<file-path>. The agent finds it via search_docs like any other doc — no new tools needed. Incremental re-ingest skips files with unchanged history.

Editor Integrations

Add the server config to your editor — your AI agent gets search_docs, get_doc, and get_related tools automatically:

{
  "mcpServers": {
    "docs": {
      "command": "gnosis-mcp",
      "args": ["serve"]
    }
  }
}

Add to .vscode/mcp.json (note: "servers" not "mcpServers"):

{
  "servers": {
    "docs": {
      "command": "gnosis-mcp",
      "args": ["serve"]
    }
  }
}

Also discoverable via the VS Code MCP gallery — search @mcp gnosis in the Extensions view.

Transport

Stdio (default) spawns one server per editor session — simplest. HTTP shares one process across every client so the DB, embedding cache, and file watcher stay in sync across sessions:

gnosis-mcp serve --transport streamable-http --host 0.0.0.0 --port 8000

{ "mcpServers": { "docs": { "type": "url", "url": "http://127.0.0.1:8000/mcp" } } }

Pick HTTP for multi-session agent setups (Claude Code with agent teams, parallel terminals, CI). Full write-up: gnosismcp.com/doc/docs/deployment.

REST API

v0.10.0+ — HTTP endpoints alongside MCP on the same port.

gnosis-mcp serve --transport streamable-http --rest

CORS, Bearer auth, custom public-path allowlist — full reference: docs/rest-api.md · gnosismcp.com/doc/docs/rest-api.

Backends

Auto-detection: Set GNOSIS_MCP_DATABASE_URL to postgresql://... and it uses PostgreSQL. Don't set it and it uses SQLite. Override with GNOSIS_MCP_BACKEND=sqlite|postgres.

pip install gnosis-mcp[postgres]
export GNOSIS_MCP_DATABASE_URL="postgresql://user:pass@localhost:5432/mydb"
gnosis-mcp init-db              # create tables + indexes
gnosis-mcp ingest ./docs/       # load your markdown
gnosis-mcp serve

For hybrid semantic+keyword search, also enable pgvector:

CREATE EXTENSION IF NOT EXISTS vector;

Then backfill embeddings:

gnosis-mcp embed                        # via OpenAI (default)
gnosis-mcp embed --provider ollama      # or use local Ollama

Claude Code Plugin

For Claude Code users, install as a plugin to get the MCP server plus slash commands:

claude plugin marketplace add nicholasglazer/gnosis-mcp
claude plugin install gnosis

This gives you:

The plugin works with both SQLite and PostgreSQL backends. Prefer manual copy-paste over the plugin marketplace? See llms-install.md Path B.

Add to .claude/mcp.json:

{
  "mcpServers": {
    "gnosis": {
      "command": "gnosis-mcp",
      "args": ["serve"]
    }
  }
}

For PostgreSQL, add "env": {"GNOSIS_MCP_DATABASE_URL": "postgresql://..."}.

Tools & Resources

Gnosis MCP exposes 9 tools and 3 resources over MCP. Your AI agent calls these automatically when it needs information from your docs.

Read tools are always available. Write tools require GNOSIS_MCP_WRITABLE=true.

How search works

# Keyword search — works on both SQLite and PostgreSQL
gnosis-mcp search "stripe webhook"

# Hybrid search — keyword + semantic (requires [embeddings] or pgvector)
gnosis-mcp search "how does billing work" --embed

# Filtered — narrow results to a specific category
gnosis-mcp search "auth" -c guides

When called via MCP, the agent passes a query string for keyword search. With embeddings configured, search automatically combines keyword and semantic results using Reciprocal Rank Fusion. Results include a highlight field with matched terms in <mark> tags.

Context Loading

The get_context tool provides usage-weighted document summaries — ideal for session startup or "what matters most?" queries.

# Most-accessed docs (no topic)
get_context(limit=10)

# Topic-focused with access enrichment
get_context(topic="deployment", category="guides")

Behind the scenes, Gnosis tracks which documents are accessed via search_docs and get_doc, then uses access frequency to rank importance. Disable tracking with GNOSIS_MCP_ACCESS_LOG=false.

Graph & Links

Gnosis automatically extracts links from your documentation — both frontmatter relates_to declarations and markdown links in content. Use the graph tools to explore connections:

# Direct neighbors
get_related("guides/auth.md")

# Multi-hop traversal (2 levels deep, with titles)
get_related("guides/auth.md", depth=2, include_titles=True)

# Filter out noisy git history links
get_related("guides/auth.md", relation_type="relates_to")

# Graph topology: find orphans and hubs
get_graph_stats()

Relation types: related (default frontmatter), content_link (body markdown links + [[wikilinks]]), git_co_change (commit co-occurrence), git_ref (git history → source file). Plus 16 typed edges via the relations: frontmatter block: prerequisite, depends_on, summarizes / summarized_by, extends / extended_by, replaces / replaced_by, audited_by / audits, implements / implemented_by, tests / tested_by, example_of, references.

Embeddings

Embeddings enable semantic search — finding docs by meaning, not just keywords.

Local ONNX (recommended) — zero-config, no API key:

pip install gnosis-mcp[embeddings]
gnosis-mcp ingest ./docs/ --embed       # ingest + embed in one step
gnosis-mcp embed                        # or embed existing chunks separately

Uses MongoDB/mdbr-leaf-ir (~23MB quantized, Apache 2.0). Auto-downloads on first run.

Remote providers — OpenAI, Ollama, or any OpenAI-compatible endpoint:

gnosis-mcp embed --provider openai      # requires GNOSIS_MCP_EMBED_API_KEY
gnosis-mcp embed --provider ollama      # uses local Ollama server

Pre-computed vectors — pass embeddings to upsert_doc or query_embedding to search_docs from your own pipeline.

Configuration

Nothing required for SQLite — zero config works. Override via GNOSIS_MCP_* env vars. Most-used:

Full list (~40 variables covering embeddings, crawl, REST, column overrides, webhooks, logging): docs/config.md · browsable at gnosismcp.com/doc/docs/config.

Delegate search to your own PostgreSQL function for custom ranking:

CREATE FUNCTION my_schema.my_search(
    p_query_text text,
    p_categories text[],
    p_limit integer
) RETURNS TABLE (
    file_path text, title text, content text,
    category text, combined_score double precision
) ...

GNOSIS_MCP_SEARCH_FUNCTION=my_schema.my_search

Query across multiple doc tables:

GNOSIS_MCP_CHUNKS_TABLE=documentation_chunks,api_docs,tutorial_chunks

All tables must share the same schema. Reads use UNION ALL. Writes target the first table.

gnosis-mcp ingest <path> [--dry-run] [--force] [--embed] [--prune] [--wipe] [--include-crawled]
gnosis-mcp ingest-git <repo> [--since] [--until] [--author] [--max-commits-per-file]
                             [--include] [--exclude] [--include-merges]
                             [--dry-run] [--force] [--embed]
gnosis-mcp crawl <url> [--sitemap] [--max-depth N] [--include] [--exclude] [--max-pages N]
                       [--dry-run] [--force] [--embed]
gnosis-mcp serve [--transport stdio|sse|streamable-http] [--host HOST] [--port PORT]
                 [--ingest PATH] [--watch PATH] [--rest]
gnosis-mcp search <query> [-n LIMIT] [-c CAT] [--embed]    Search docs
gnosis-mcp stats                                           Document, chunk, and embedding counts
gnosis-mcp check                                           Verify DB connection + extensions
gnosis-mcp embed [--provider P] [--model M] [--batch-size N] [--dry-run]
gnosis-mcp init-db [--dry-run]                             Create tables + indexes
gnosis-mcp export [-f json|markdown] [-c CAT]              Export documents
gnosis-mcp diff <path>                                     Preview changes on re-ingest
gnosis-mcp prune <path> [--dry-run] [--include-crawled]    Delete chunks for missing files
gnosis-mcp cleanup [--days N]                              Purge old access log entries
gnosis-mcp eval [--json]                                   Retrieval quality harness (Hit@5, MRR, P@5)
gnosis-mcp fix-link-types                                  Migrate pre-0.10 git-history links

gnosis-mcp ingest scans a directory for supported files and loads them into the database:

• Multi-format — Markdown native; .txt, .ipynb, .toml, .csv, .json auto-converted. Optional: .rst ([rst] extra), .pdf ([pdf] extra)

• Smart chunking — splits by H2 headings (H3/H4 for oversized sections), never splits inside code blocks or tables

• Frontmatter — extracts title, category, audience, tags from YAML frontmatter

• Auto-linking — relates_to in frontmatter creates bidirectional links for get_related

• Auto-categorization — infers category from parent directory name

• Incremental — content hashing skips unchanged files (--force to override)

• Watch mode — gnosis-mcp serve --watch ./docs/ auto-re-ingests on changes

src/gnosis_mcp/
├── backend.py         DocBackend protocol + create_backend() factory
├── pg_backend.py      PostgreSQL — asyncpg, tsvector, pgvector
├── sqlite_backend.py  SQLite — aiosqlite, FTS5, sqlite-vec hybrid search (RRF)
├── sqlite_schema.py   SQLite DDL — tables, FTS5, triggers, vec0 virtual table
├── config.py          Config from env vars, backend auto-detection
├── db.py              Backend lifecycle + FastMCP lifespan
├── server.py          FastMCP server — 9 tools, 3 resources, auto-embed queries
├── ingest.py          File scanner + converters — multi-format, smart chunking
├── crawl.py           Web crawler — sitemap/BFS, robots.txt, ETag caching
├── parsers/           Non-file ingest sources (git history, future: schemas)
│   └── git_history.py Git log → markdown documents per file
├── watch.py           File watcher — mtime polling, auto-re-ingest
├── schema.py          PostgreSQL DDL — tables, indexes, search functions
├── embed.py           Embedding providers — OpenAI, Ollama, custom, local ONNX
├── local_embed.py     Local ONNX embedding engine — HuggingFace model download
└── cli.py             CLI — serve, ingest, crawl, search, embed, stats, check, cleanup

Available On

MCP Registry (feeds VS Code MCP gallery and GitHub Copilot) · PyPI · mcp.so · Glama · cursor.directory

AI-Friendly Docs

Development

git clone https://github.com/nicholasglazer/gnosis-mcp.git
cd gnosis-mcp
python -m venv .venv && source .venv/bin/activate
pip install -e ".[dev]"
pytest                    # 632 tests, no database needed
ruff check src/ tests/

All tests run without a database. Keep it that way.

Good first contributions: new embedding providers, export formats, ingestion for new file types (via optional extras). Open an issue first for larger changes.

Sponsors

If Gnosis MCP saves you time, consider sponsoring the project.

License

MIT