Crawlemoon MCP Server

A free, open-source MCP server that gives any agent (Claude Code, Cursor, Windsurf, …) 55 production-grade tools for the full web-crawling stack: deep analysis, stealth, API discovery, session recording → runnable crawler, smart extraction. No proprietary API. No per-request fee.

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Quick start

The recommended path needs no install — uvx runs straight from PyPI:

{
  "mcpServers": {
    "crawlemoon": {
      "command": "uvx",
      "args": ["crawlemoon"]
    }
  }
}

Requires uv. Install once: curl -LsSf https://astral.sh/uv/install.sh | sh. Or use pipx run crawlemoon / pip install crawlemoon instead.

Where to put that JSON: Cursor → Settings → MCP. Claude Code → ~/.config/claude/mcp_settings.json. Windsurf → Settings → MCP Servers.

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How it works

Your agent talks to Crawlemoon over the Model Context Protocol. Crawlemoon owns a hardened browser pool, an HTTP stack with TLS fingerprinting, and a rotating proxy pool. While it fetches pages, it captures network traffic, reads scripts, and introspects schemas — so the agent gets clean structured data, not raw HTML.

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What's in the box

A short list — see the source for the full set of 55 tools.

Group	Tools
Deep analysis	deep_analyze, discover_apis, introspect_graphql, analyze_websocket, analyze_auth, detect_protection, detect_technology
Stealth	stealth_request, configure_proxies, configure_rate_limit, add_proxy, test_proxy
Record → crawler	record_session, stop_recording, export_recording, generate_crawler
Extraction	smart_extract, extract_article, extract_tables, extract_links, extract_forms, extract_metadata, convert_to_markdown
Page interaction	take_screenshot, fill_form, wait_and_extract, compare_pages, measure_performance, check_accessibility, get_dom_tree
Sessions & cache	save_session, load_session, get_cookies, get_storage, clear_cache, get_cache_stats
Advanced (opt-in)	execute_js, execute_cdp, deobfuscate_js, extract_from_js, solve_captcha

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Smart extraction — bring any LLM, including free ones

smart_extract works without any API key using pattern matching. Plug in any OpenAI-compatible endpoint for higher accuracy — including FREE tiers:

# OpenRouter (free models exist)
CRAWLEMOON_LLM_PROVIDER=openrouter
CRAWLEMOON_LLM_API_KEY=sk-or-v1-xxx
CRAWLEMOON_LLM_MODEL=meta-llama/llama-3.2-3b-instruct:free

# Groq (free, very fast)
CRAWLEMOON_LLM_PROVIDER=groq
CRAWLEMOON_LLM_API_KEY=gsk_xxx

# Local Ollama (no key needed)
CRAWLEMOON_LLM_PROVIDER=ollama
CRAWLEMOON_LLM_MODEL=llama3.2

Together, DeepSeek, Mistral, Fireworks, and standard OpenAI also work via CRAWLEMOON_LLM_BASE_URL.

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Configuration

Variable	Default	Notes
CRAWLEMOON_HEADLESS	true	Run browser without UI
CRAWLEMOON_BROWSER	chromium	chromium / firefox / webkit
CRAWLEMOON_POOL_SIZE	5	Max concurrent browsers
CRAWLEMOON_NAV_TIMEOUT	30.0	Page-load timeout (s)
CRAWLEMOON_API_KEY	unset	If set, every tool call must include matching _api_key
CRAWLEMOON_ALLOW_DANGEROUS_JS	false	Required for execute_js / execute_cdp / deobfuscate_js
CRAWLEMOON_JS_MAX_LENGTH	50000	Length cap for JS payloads
CRAWLEMOON_JS_EXEC_TIMEOUT	10.0	Per-script timeout (s)

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Security

execute_js, execute_cdp, and deobfuscate_js are disabled by default — they execute or operate on arbitrary code in a real browser. Enable on trusted networks with CRAWLEMOON_ALLOW_DANGEROUS_JS=true. Even then, payloads are length-capped, time-bounded, and a denylist rejects eval, new Function, dynamic import(), document.write, importScripts, and WebAssembly.{compile,instantiate}. Set CRAWLEMOON_API_KEY so MCP clients must present a matching _api_key.

These are mitigations, not a sandbox: do not expose this server to untrusted clients.

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Develop

git clone https://github.com/razavioo/crawlemoon.git
cd crawlemoon
make dev-install      # editable install + dev/captcha/ocr extras + pre-commit
make test             # pytest
make lint             # ruff + mypy

Releases

This project uses Trusted Publishing (OIDC) via GitHub Actions to automate publishing releases directly to PyPI.

To release a new version:

1. Bump the version number in pyproject.toml.
2. Commit the change and create a git tag matching the version (e.g. v1.1.8):

   git add pyproject.toml
   git commit -m "chore: bump version to 1.1.8"
   git tag v1.1.8

3. Push your branch and the tag to GitHub:

   git push origin main --tags

GitHub Actions will automatically run tests, build the package, and publish it securely to PyPI under the crawlemoon package space.

PRs welcome. Particularly interested in: distributed mode (Redis queue), result sinks (Postgres / S3), Prometheus metrics. See MIT License.

_{Made by emad.dev}