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Crawl4AI MCP Server

@weidwonder

Repository

Basic information

Category

Other

License

MIT

Transports

stdio

Author

weidwonder

About Crawl4AI MCP Server

用于提供给本地开发者的 LLM的高效互联网搜索&内容获取的MCP Server， 节省你的token

Crawl4AI MCP Server config

Add this server to your MCP-compatible client using the configuration below.

{
  "mcpServers": {
    "crawl4ai-mcp-server": {
      "command": "python",
      "args": [
        "-m",
        "venv",
        "crawl4ai_env"
      ]
    }
  }
}

Crawl4AI MCP Server tools

Tool list not available yet

This listing does not currently have a tool list to display. This does not mean the server has no tools; check its documentation for supported capabilities.

We auto-extract tools from the README. The maintainer can list them under a ## Tools heading to populate this section.

Overview

What is Crawl4AI MCP Server?

Crawl4AI MCP Server is an MCP (Model Context Protocol) server that equips AI assistant systems with multi‑engine search and LLM‑optimized web page content understanding. It supports DuckDuckGo (default) and Google search, and provides intelligent content extraction with citation‑enabled output formats.

How to use Crawl4AI MCP Server?

Install via cloning the repository, creating a Python 3.9+ virtual environment, installing dependencies (pip install -r requirements.txt) and the Playwright browser (playwright install), or via Smithery for Claude Desktop. The server exposes two tools: search (with parameters query, num_results, engine) and read_url (with parameters url and format). To use Google search, configure config.json with an API key and CSE ID.

Key features of Crawl4AI MCP Server

  • Multi‑engine search (DuckDuckGo and Google)
  • LLM‑optimized web content extraction with noise filtering
  • Automatic identification and retention of key content
  • Multiple output formats including markdown with citations
  • High‑performance asynchronous design based on FastMCP

Use cases of Crawl4AI MCP Server

  • AI assistants performing web research and fact‑checking
  • Summarizing and extracting core information from articles
  • Gathering search results from multiple engines simultaneously
  • Providing LLMs with clean, citation‑tracked web content

Frequently asked questions

1

Which search engines are supported?

DuckDuckGo (default, no API key required) and Google (requires API key and CSE ID configured in `config.json`). The `"engine"` parameter can be set to `"duckduckgo"`, `"google"`, or `"all"`.

2

What Python version is required?

Python 3.9 or higher is required.

3

What output formats does the `read_url` tool support?

It supports `markdown_with_citations` (default), `fit_markdown`, `raw_markdown`, `references_markdown`, `fit_html`, and `markdown`.

4

Is an API key needed to use DuckDuckGo search?

No, DuckDuckGo search works without any API key. Google search requires a Google API key and a Custom Search Engine ID.

5

What dependencies are needed beyond Python?

The Playwright browser must be installed (`playwright install`). All other dependencies are listed in `requirements.txt`.

Comments

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