Crawl via sitemap (best for large doc sites)

An MCP server for Crawl via sitemap (best for large doc sites).

MCP Free Verified 545 lines SEO

Description

An MCP server for Crawl via sitemap (best for large doc sites).

Related Skills & MCPs

Frequently Asked Questions

Is Crawl via sitemap (best for large doc sites) free to use?

Crawl via sitemap (best for large doc sites) is completely free to use. You can download the configuration or copy it directly from the page.

Download Config

Gnosis MCP

Turn your docs into a searchable knowledge base for AI agents.pip install, ingest, serve.

Quick Start &middot;
  Git History &middot;
  Web Crawl &middot;
  Backends &middot;
  Editors &middot;
  Tools &middot;
  Embeddings &middot;
  Full Reference

Ingest docs &rarr; Search with highlights &rarr; Stats overview &rarr; Serve to AI agents

---

### Without a docs server

- LLMs hallucinate API signatures that don't exist
- Entire files dumped into context — 3,000 to 8,000+ tokens each
- Architecture decisions buried across dozens of files

### With Gnosis MCP

- `search_docs` returns ranked, highlighted excerpts (~600 tokens)
- Real answers grounded in your actual documentation
- Works across hundreds of docs instantly

---

## Features

- **Zero config** — SQLite by default, `pip install` and go
- **Hybrid search** — keyword (BM25) + semantic (local ONNX embeddings, no API key)
- **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
- **PostgreSQL ready** — pgvector + tsvector when you need scale

## Quick Start

```bash
pip 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.

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

```bash
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:

```bash
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
```

Try without installing (uvx)

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

## Web Crawl

Dry-run discovery &rarr; Crawl &amp; ingest &rarr; Search crawled docs &rarr; 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.

For remote deployment, use Streamable HTTP:

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

## REST API

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

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

Web apps can now query your docs over plain HTTP — no MCP protocol required.

| Endpoint | Description |
|----------|-------------|
| `GET /health` | Server status, version, doc count |
| `GET /api/search?q=&limit=&category=` | Search docs (auto-embeds with local provider) |
| `GET /api/docs/{path}` | Get document by file path |
| `GET /api/docs/{path}/related` | Get related documents |
| `GET /api/categories` | List categories with counts |

**Environment variables:**

| Variable | Description |
|----------|-------------|
| `GNOSIS_MCP_REST=true` | Enable REST API (same as `--rest`) |
| `GNOSIS_MCP_CORS_ORIGINS` | CORS allowed origins: `*` or comma-separated list |
| `GNOSIS_MCP_API_KEY` | Optional Bearer token auth |

**Examples:**

```bash
# Health check
curl http://127.0.0.1:8000/health

# Search
curl "http://127.0.0.1:8000/api/search?q=authentication&limit=5"

# With API key
curl -H "Authorization: Bearer sk-secret" "http://127.0.0.1:8000/api/search?q=setup"
```

## 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 |
| **`/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`:

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

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

## Tools & Resources

Gnosis MCP exposes 6 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 | 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.

## 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

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 |
| `GNOSIS_MCP_TRANSPORT` | `stdio` | Transport: `stdio`, `sse`, or `streamable-http` |
| `GNOSIS_MCP_EMBEDDING_DIM` | `1536` | Vector dimension for init-db |

All configuration variables

**Database:** `GNOSIS_MCP_SCHEMA` (public), `GNOSIS_MCP_CHUNKS_TABLE` (documentation_chunks), `GNOSIS_MCP_LINKS_TABLE` (documentation_links), `GNOSIS_MCP_SEARCH_FUNCTION` (custom search on PG).

**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).

**Embeddings:** `GNOSIS_MCP_EMBED_PROVIDER` (openai/ollama/custom/local), `GNOSIS_MCP_EMBED_MODEL`, `GNOSIS_MCP_EMBED_DIM` (384), `GNOSIS_MCP_EMBED_API_KEY`, `GNOSIS_MCP_EMBED_URL`, `GNOSIS_MCP_EMBED_BATCH_SIZE` (50).

**Column overrides:** `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`.

**Logging:** `GNOSIS_MCP_LOG_LEVEL` (INFO).

Custom search function (PostgreSQL)

Delegate search to your own PostgreSQL function for custom ranking:

```sql
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
) ...
```

```bash
GNOSIS_MCP_SEARCH_FUNCTION=my_schema.my_search
```

Multi-table mode (PostgreSQL)

Query across multiple doc tables:

```bash
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  [--dry-run] [--force] [--embed]    Load files into database
gnosis-mcp ingest-git  [--since] [--max-commits] [--include] [--exclude] [--dry-run] [--embed]
gnosis-mcp crawl  [--sitemap] [--depth N] [--include] [--exclude] [--dry-run] [--force] [--embed]
gnosis-mcp serve [--transport stdio|sse|streamable-http] [--ingest PATH] [--watch PATH]
gnosis-mcp search  [-n LIMIT] [-c CAT] [--embed]    Search docs
gnosis-mcp stats                                           Document, chunk, and embedding counts
gnosis-mcp check                                           Verify DB connection + sqlite-vec
gnosis-mcp embed [--provider P] [--model M] [--dry-run]    Backfill embeddings
gnosis-mcp init-db [--dry-run]                             Create tables + indexes
gnosis-mcp export [-f json|markdown|csv] [-c CAT]          Export documents
gnosis-mcp diff                                      Preview changes on re-ingest
```

How ingestion works

`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

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-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 — 6 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
```

## Available On

[MCP Registry](https://registry.modelcontextprotocol.io) (feeds VS Code MCP gallery and GitHub Copilot) · [PyPI](https://pypi.org/project/gnosis-mcp/) · [mcp.so](https://mcp.so) · [Glama](https://glama.ai) · [cursor.directory](https://cursor.directory)

## AI-Friendly Docs

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

## Performance

Benchmarked on SQLite (in-memory) with keyword search (FTS5 + BM25):

| Corpus | QPS | p50 | p95 | p99 | Hit Rate |
|--------|-----|-----|-----|-----|----------|
| 100 docs / 300 chunks | ~9,800 | 0.09ms | 0.16ms | 0.18ms | 100% |
| 500 docs / 1,500 chunks | ~3,500 | 0.24ms | 0.51ms | 0.82ms | 100% |

Install size: ~23MB with `[embeddings]` (ONNX model). Base install is ~5MB.

Run the benchmark yourself:

```bash
python tests/bench/bench_search.py                # 100 docs, 1000 queries
python tests/bench/bench_search.py --docs 500     # larger corpus
python tests/bench/bench_search.py --json          # machine-readable output
```

550+ tests, 10 eval cases (90% hit rate, 0.85 MRR on sample corpus). All tests run without a database.

## Development

```bash
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                    # 550+ 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](https://github.com/sponsors/nicholasglazer).

## License

[MIT](LICENSE)

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