gnosis-mcp 0.6.2

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

Gnosis MCP

Give your AI agent a searchable knowledge base. Zero config.

Quick Start · Backends · Editor Setup · Tools & Resources · Configuration · Full Reference

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AI coding agents can read your source code but not your documentation. They guess at architecture, miss established patterns, and hallucinate details they could have looked up.

Gnosis MCP fixes this. Point it at a folder of markdown files and it creates a searchable knowledge base that any MCP-compatible AI agent can query — Claude Code, Cursor, Windsurf, Cline, and any tool that supports the Model Context Protocol.

No database server. SQLite works out of the box. Scale to PostgreSQL + pgvector when you need hybrid semantic search.

Why use this

Less hallucination. Agents search your docs before guessing. Architecture decisions, API contracts, billing rules — one tool call away instead of made up.

Lower token costs. A search returns ~600 tokens of ranked results. Reading the same docs as files costs 3,000-8,000+ tokens. On a 170-doc knowledge base (~840K tokens), that's the difference between a precise answer and a blown context window.

Docs that stay current. Add a new markdown file, run ingest, it's searchable immediately. No routing tables to maintain, no hardcoded paths to update.

Works with what you have. Your docs are already markdown files in a folder. Gnosis MCP indexes them as-is — no format conversion, no special syntax needed.

Quick Start

pip install gnosis-mcp
gnosis-mcp ingest ./docs/       # loads markdown, auto-creates SQLite database
gnosis-mcp serve                # starts MCP server

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

Test it before connecting to an editor:

gnosis-mcp search "getting started"    # verify search works
gnosis-mcp stats                       # see what was indexed

Try without installing (uvx)

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

Editor Integrations

Gnosis MCP works with any MCP-compatible editor. Add the server config, and your AI agent gets search_docs, get_doc, and get_related tools automatically.

Claude Code

Add to .claude/mcp.json:

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

Or install as a Claude Code plugin for a richer experience with slash commands.

Cursor

Add to .cursor/mcp.json:

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

Windsurf

Add to ~/.codeium/windsurf/mcp_config.json:

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

Cline

Open Cline MCP settings panel and add the same server config.

Other MCP clients

Any tool that supports the Model Context Protocol works. The server communicates over stdio by default, or SSE with --transport sse.

Choose Your Backend

	SQLite (default)	PostgreSQL
Install	pip install gnosis-mcp	pip install gnosis-mcp[postgres]
Config	Nothing — works immediately	Set DATABASE_URL
Search	FTS5 keyword (BM25)	tsvector + pgvector hybrid
Embeddings	Stored as binary blobs	Native vector type + HNSW index
Multi-table	No	Yes (UNION ALL across tables)
Best for	Personal projects, small teams	Production, semantic search, large doc sets

Auto-detection: Set 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

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:

Component	What you get
MCP server	gnosis-mcp serve — auto-configured
/gnosis:search	Search docs with keyword or --semantic hybrid mode
/gnosis:status	Health check — connectivity, doc stats, troubleshooting
/gnosis:manage	CRUD — add, delete, update metadata, bulk embed

The plugin works with both SQLite and PostgreSQL backends.

Manual setup (without plugin)

Add to .claude/mcp.json:

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

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

What It Does

Gnosis MCP exposes 6 tools and 3 resources over MCP. Your AI agent calls these automatically when it needs information from your docs.

Tools

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	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.

Resources

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

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

# Hybrid search — keyword + semantic similarity (PostgreSQL + embeddings)
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. On PostgreSQL with embeddings, it can also pass query_embedding for hybrid mode that combines keyword matching with semantic similarity.

Embeddings

Embeddings enable semantic search — finding docs by meaning, not just keywords. Gnosis MCP supports three approaches, none of which add runtime dependencies:

1. Pre-computed vectors — pass embeddings to upsert_doc or query_embedding to search_docs if you generate them in your own pipeline.

2. CLI backfill — find chunks missing embeddings and generate them:

gnosis-mcp embed --dry-run              # preview what needs embedding
gnosis-mcp embed                        # backfill via OpenAI (default)
gnosis-mcp embed --provider ollama      # or use local Ollama

Supports OpenAI, Ollama, and any OpenAI-compatible endpoint (e.g., local models via vLLM or LiteLLM).

3. Built-in hybrid scoring — on PostgreSQL, when both keyword and embedding results are available, search automatically combines them using reciprocal rank fusion.

Configuration

All settings via environment variables. Nothing required for SQLite — it works with zero config.

Variable	Default	Description
GNOSIS_MCP_DATABASE_URL	SQLite auto	PostgreSQL URL or SQLite file path
GNOSIS_MCP_BACKEND	auto	Force sqlite or postgres
GNOSIS_MCP_WRITABLE	false	Enable write tools (upsert_doc, delete_doc, update_metadata)
GNOSIS_MCP_TRANSPORT	stdio	Server transport: stdio or sse
GNOSIS_MCP_SCHEMA	public	Database schema (PostgreSQL only)
GNOSIS_MCP_CHUNKS_TABLE	documentation_chunks	Table name for chunks
GNOSIS_MCP_SEARCH_FUNCTION	—	Custom search function (PostgreSQL only)
GNOSIS_MCP_EMBEDDING_DIM	1536	Vector dimension for init-db

All variables

Search & chunking: GNOSIS_MCP_CONTENT_PREVIEW_CHARS (200), GNOSIS_MCP_CHUNK_SIZE (4000), GNOSIS_MCP_SEARCH_LIMIT_MAX (20).

Connection pool (PostgreSQL): GNOSIS_MCP_POOL_MIN (1), GNOSIS_MCP_POOL_MAX (3).

Webhooks: GNOSIS_MCP_WEBHOOK_URL, GNOSIS_MCP_WEBHOOK_TIMEOUT (5s). Set a URL to receive POST notifications when documents are created, updated, or deleted.

Embeddings: GNOSIS_MCP_EMBED_PROVIDER (openai/ollama/custom), GNOSIS_MCP_EMBED_MODEL (text-embedding-3-small), GNOSIS_MCP_EMBED_API_KEY, GNOSIS_MCP_EMBED_URL (custom endpoint), GNOSIS_MCP_EMBED_BATCH_SIZE (50).

Column overrides (for connecting to existing tables with non-standard column names): GNOSIS_MCP_COL_FILE_PATH, GNOSIS_MCP_COL_TITLE, GNOSIS_MCP_COL_CONTENT, GNOSIS_MCP_COL_CHUNK_INDEX, GNOSIS_MCP_COL_CATEGORY, GNOSIS_MCP_COL_AUDIENCE, GNOSIS_MCP_COL_TAGS, GNOSIS_MCP_COL_EMBEDDING, GNOSIS_MCP_COL_TSV, GNOSIS_MCP_COL_SOURCE_PATH, GNOSIS_MCP_COL_TARGET_PATH, GNOSIS_MCP_COL_RELATION_TYPE.

Links table: GNOSIS_MCP_LINKS_TABLE (documentation_links).

Logging: GNOSIS_MCP_LOG_LEVEL (INFO).

Custom search function (PostgreSQL)

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

Multi-table mode (PostgreSQL)

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.

CLI Reference

gnosis-mcp ingest <path> [--dry-run]                       Load markdown files into the database
gnosis-mcp serve [--transport stdio|sse] [--ingest PATH]   Start MCP server (optionally ingest first)
gnosis-mcp search <query> [-n LIMIT] [-c CAT] [--embed]    Search from the command line
gnosis-mcp stats                                           Show document and chunk counts
gnosis-mcp check                                           Verify database connection
gnosis-mcp embed [--provider P] [--model M] [--dry-run]    Backfill embeddings
gnosis-mcp init-db [--dry-run]                             Create tables + indexes manually
gnosis-mcp export [-f json|markdown] [-c CAT]              Export documents

How ingestion works

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

• Smart chunking — splits by H2 headings, keeping sections together (not arbitrary character limits)
• Frontmatter support — extracts title, category, audience, tags from YAML frontmatter
• Auto-categorization — infers category from the parent directory name
• Incremental updates — content hashing skips unchanged files on re-run
• Dry run — preview what would be indexed with --dry-run

Available on

Gnosis MCP is listed on the Official MCP Registry, PyPI, and major MCP directories. It works with any MCP-compatible client today, with more editor integrations coming as the MCP ecosystem grows.

Architecture

src/gnosis_mcp/
├── backend.py         DocBackend protocol + create_backend() factory
├── pg_backend.py      PostgreSQL — asyncpg, tsvector, pgvector
├── sqlite_backend.py  SQLite — aiosqlite, FTS5
├── sqlite_schema.py   SQLite DDL — tables, FTS5, triggers
├── config.py          Config from env vars, backend auto-detection
├── db.py              Backend lifecycle + FastMCP lifespan
├── server.py          FastMCP server — 6 tools, 3 resources
├── ingest.py          Markdown scanner — H2 chunking, frontmatter
├── schema.py          PostgreSQL DDL — tables, indexes, search functions
├── embed.py           Embedding providers — OpenAI, Ollama, custom
└── cli.py             CLI — serve, ingest, search, embed, stats, check

AI-Friendly Docs

These files are optimized for AI agents to consume:

File	Purpose
llms.txt	Quick overview — what it does, tools, config
llms-full.txt	Complete reference in one file
llms-install.md	Step-by-step installation guide

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                    # 176 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 RST/AsciiDoc/HTML, search highlighting. Open an issue first for larger changes.

Sponsors

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

License

MIT