# Gnosis Mcp

> Zero-config MCP server for searchable documentation (SQLite default, PostgreSQL optional)

- **Type:** MCP server
- **Install:** `agentstack add mcp-nicholasglazer-gnosis-mcp`
- **Verified:** Yes — security-reviewed for prompt injection and unsafe behavior
- **Seller:** [nicholasglazer](https://agentstack.voostack.com/s/nicholasglazer)
- **Installs:** 0
- **Category:** [Databases](https://agentstack.voostack.com/c/databases)
- **Latest version:** 0.1.0
- **License:** MIT
- **Upstream author:** [nicholasglazer](https://github.com/nicholasglazer)
- **Source:** https://github.com/nicholasglazer/gnosis-mcp
- **Website:** https://gnosismcp.com

## Install

```sh
agentstack add mcp-nicholasglazer-gnosis-mcp
```

Requires the [AgentStack CLI](https://agentstack.voostack.com/docs/cli). Works with Claude Code, Cursor, and any MCP-compatible agent.

## About

Gnosis MCP

Stop pasting files into context. Your AI agent searches your local docs instead.5–10× fewer tokens per lookup. 92 % Hit@5 on real dev docs. Zero cloud dependencies.

  
  
  
  
  

  Quick Start ·
  Git History ·
  Web Crawl ·
  Backends ·
  Editors ·
  Tools ·
  Embeddings ·
  Full Reference

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](https://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](docs/bench-experiments-2026-04-18.md)** — 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 
Run with Docker (zero install)

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

```bash
# 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.yaml):

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

Images tagged `:latest`, `:`, `:`, `:main`, `:sha-`.

Try without installing (uvx)

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

## Web Crawl

Dry-run discovery → Crawl & ingest → Search crawled docs → SSRF protection

Ingest docs from any website — no local files needed:

```bash
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:

```bash
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/`. 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:

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

| Editor | Config file |
|--------|------------|
| **Claude Code** | `.claude/mcp.json` (or [install as plugin](#claude-code-plugin)) |
| **Cursor** | `.cursor/mcp.json` |
| **Windsurf** | `~/.codeium/windsurf/mcp_config.json` |
| **JetBrains** | Settings > Tools > AI Assistant > MCP Servers |
| **Cline** | Cline MCP settings panel |

VS Code (GitHub Copilot) — slightly different key

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

```json
{
  "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:

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

```json
{ "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](https://gnosismcp.com/doc/docs/deployment)**.

## REST API

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

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

| Endpoint | Returns |
|----------|---------|
| `GET /health` | status, version, distinct-doc + chunk counts |
| `GET /api/search?q=` | hybrid search (auto-embeds with `local` provider) |
| `GET /api/docs/{path}` | full document |
| `GET /api/docs/{path}/related` | graph neighbours |
| `GET /api/categories` | category → doc count |
| `GET /api/context?topic=` | usage-weighted topic primer |
| `GET /api/graph/stats` | orphans, hubs, relation distribution |
| `POST /v1/embed` | OpenAI-compatible embeddings (`v0.14.0+`) — `{texts, model?}` → `{model, dim, vectors, usage}` |

CORS, Bearer auth, custom public-path allowlist — full reference: **[`docs/rest-api.md`](docs/rest-api.md)** · **[gnosismcp.com/doc/docs/rest-api](https://gnosismcp.com/doc/docs/rest-api)**.

### Self-hosted embeddings service (v0.14.0+)

`POST /v1/embed` turns gnosis-mcp into a drop-in OpenAI-shaped embeddings backend. Point any client that already speaks the `/v1/embeddings` shape at your gnosis-mcp instance — your hardware, your model choice, no per-token bills:

```bash
curl -X POST http://localhost:8000/v1/embed \
  -H "Authorization: Bearer $GNOSIS_MCP_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{"texts": ["hello", "hola"], "model": "intfloat/multilingual-e5-large"}'
```

Pick your model with `GNOSIS_MCP_EMBED_MODEL` (default `MongoDB/mdbr-leaf-ir`):
- `MongoDB/mdbr-leaf-ir` — 23M params, #1 MTEB ≤100M, English-specialised
- `intfloat/multilingual-e5-large` — 560M, 100+ languages
- `BAAI/bge-m3` — 568M, multilingual, dense + sparse hybrid

Limits: 256 texts × 50 KB per request. Same Bearer auth as the rest of the REST API.

## Backends

| | SQLite (default) | SQLite + embeddings | PostgreSQL |
|---|---|---|---|
| **Install** | `pip install gnosis-mcp` | `pip install gnosis-mcp[embeddings]` | `pip install gnosis-mcp[postgres]` |
| **Config** | Nothing | Nothing | Set `GNOSIS_MCP_DATABASE_URL` |
| **Search** | FTS5 keyword (BM25) | Hybrid keyword + semantic (RRF) | tsvector + pgvector hybrid |
| **Embeddings** | None | Local ONNX (23MB, no API key) | Any provider + HNSW index |
| **Multi-table** | No | No | Yes (`UNION ALL`) |
| **Best for** | Quick start, keyword-only | Semantic search without a server | Production, large doc sets |

**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`.

PostgreSQL setup

```bash
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:

```sql
CREATE EXTENSION IF NOT EXISTS vector;
```

Then backfill embeddings:

```bash
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:

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

This gives you:

| Component | What you get |
|-----------|-------------|
| **MCP server** | `gnosis-mcp serve` — auto-configured, search tools in every chat |
| **`/gnosis:setup`** | First-time wizard: install → init-db → ingest → wire your editor |
| **`/gnosis:ingest`** | Bulk ingest (files, git history, web crawl) + re-ingest + prune |
| **`/gnosis:search`** | Keyword / hybrid / git-history search, formatted output |
| **`/gnosis:manage`** | Single-file CRUD — add, delete, update metadata |
| **`/gnosis:tune`** | Chunk-size sweep against your own golden queries |
| **`/gnosis:eval`** | Single-shot retrieval quality check with baseline tracking |
| **`/gnosis:context`** | Usage-weighted topic primer for session startup |
| **`/gnosis:status`** | Connectivity, schema, corpus health diagnostic |
| 5 subagents | `doc-explorer`, `doc-keeper`, `corpus-sync`, `context-loader`, `doc-reviewer` |

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

Manual setup (without plugin)

Add to `.claude/mcp.json`:

```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](https://modelcontextprotocol.io/). Your AI agent calls these automatically when it needs information from your docs.

| Tool | What it does | Mode |
|------|-------------|------|
| `search_docs` | Search by keyword or hybrid semantic+keyword | Read |
| `get_doc` | Retrieve a full document by path | Read |
| `get_related` | Find linked/related documents (multi-hop, relation type filtering) | Read |
| `search_git_history` | Search indexed git commit history | Read |
| `get_context` | Usage-weighted context summary | Read |
| `get_graph_stats` | Knowledge graph topology: orphans, hubs, relation distribution | Read |
| `upsert_doc` | Create or replace a document | Write |
| `delete_doc` | Remove a document and its chunks | Write |
| `update_metadata` | Change title, category, tags | Write |

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

| Resource URI | Returns |
|-----|---------|
| `gnosis://docs` | All documents — path, title, category, chunk count |
| `gnosis://docs/{path}` | Full document content |
| `gnosis://categories` | Categories with document counts |

### How search works

```bash
# 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 `` tags.

### Context Loading

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

```bash
# 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:

```bash
# 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:

```bash
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](https://huggingface.co/MongoDB/mdbr-leaf-ir) (~23MB quantized, Apache 2.0). Auto-downloads on first run.

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

```bash
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:

| Variable | Default | Description |
|----------|---------|-------------|
| `GNOSIS_MCP_DATABASE_URL` | SQLite auto | PostgreSQL URL or SQLite file path |
| `GNOSIS_MCP_WRITABLE` | `false` | Enable `upsert_doc` / `delete_doc` / `update_metadata` |
| `GNOSIS_MCP_EMBE

…

## Source & license

This open-source MCP server is cataloged on AgentStack and links to its original source — we do not rehost the code.

- **Author:** [nicholasglazer](https://github.com/nicholasglazer)
- **Source:** [nicholasglazer/gnosis-mcp](https://github.com/nicholasglazer/gnosis-mcp)
- **License:** MIT
- **Homepage:** https://gnosismcp.com

Install and usage instructions live in the source repository linked above.

## Pricing

- **Free** — Free

## Security capabilities

Automated source analysis of v0.1.0 — what this tool can access:

- **Network access:** yes
- **Filesystem access:** no
- **Shell / process execution:** no
- **Environment & secrets:** no
- **Dynamic code execution:** no

*"Yes" means the capability is present in the source — more access means more to trust, not that it is unsafe.*


## Versions

- **0.1.0** — security scan: passed — Imported from the upstream source.

## Links

- Listing page: https://agentstack.voostack.com/l/mcp-nicholasglazer-gnosis-mcp
- Seller: https://agentstack.voostack.com/s/nicholasglazer
- Browse the marketplace: https://agentstack.voostack.com/browse

---
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