What is Baidu Mcp Server

baidu-mcp-server is a Model Context Protocol (MCP) server that enables web search capabilities through Baidu, along with features for content fetching and parsing.

Use cases

Use cases include integrating Baidu search functionality into applications, extracting content from web pages for analysis, and enhancing user experiences in search-related applications.

How to use

To use baidu-mcp-server, install it via Smithery or directly from PyPI using uv. Configure it in Claude Desktop by editing the configuration file and restarting the application.

Key features

Key features include web search with advanced rate limiting, intelligent content fetching and parsing, built-in error handling, and LLM-friendly output formatting.

Where to use

baidu-mcp-server can be used in various fields such as web development, data analysis, and applications requiring web scraping and content extraction.

Baidu Search MCP Server

A Model Context Protocol (MCP) server that provides web search capabilities through Baidu, with additional features for content fetching and parsing.

Features

• Web Search: Search Baidu with advanced rate limiting and result formatting
• Content Fetching: Retrieve and parse webpage content with intelligent text extraction
• Rate Limiting: Built-in protection against rate limits for both search and content fetching
• Error Handling: Comprehensive error handling and logging
• LLM-Friendly Output: Results formatted specifically for large language model consumption

Installation

Installing via Smithery

To install Baidu Search Server for Claude Desktop automatically via Smithery:

npx -y @smithery/cli install @Evilran/baidu-mcp-server --client claude

Installing via uv

Install directly from PyPI using uv:

uv pip install baidu-mcp-server

Usage

Running with Claude Desktop

1. Download Claude Desktop
2. Create or edit your Claude Desktop configuration:
   • On macOS: ~/Library/Application Support/Claude/claude_desktop_config.json
   • On Windows: %APPDATA%\Claude\claude_desktop_config.json

Add the following configuration:

{
  "mcpServers": {
    "baidu-search": {
      "command": "uvx",
      "args": [
        "baidu-mcp-server"
      ]
    }
  }
}

3. Restart Claude Desktop

Development

For local development, you can use the MCP CLI:

# Run with the MCP Inspector
mcp dev server.py

# Install locally for testing with Claude Desktop
mcp install server.py

Available Tools

1. Search Tool

async def search(query: str, max_results: int = 10) -> str

Performs a web search on Baidu and returns formatted results.

Parameters:

• query: Search query string
• max_results: Maximum number of results to return (default: 10)

Returns:
Formatted string containing search results with titles, URLs, and snippets.

2. Content Fetching Tool

async def fetch_content(url: str) -> str

Fetches and parses content from a webpage.

Parameters:

• url: The webpage URL to fetch content from

Returns:
Cleaned and formatted text content from the webpage.

Features in Detail

Rate Limiting

• Search: Limited to 30 requests per minute
• Content Fetching: Limited to 20 requests per minute
• Automatic queue management and wait times

Result Processing

• Removes ads and irrelevant content
• Cleans up Baidu redirect URLs
• Formats results for optimal LLM consumption
• Truncates long content appropriately

Error Handling

• Comprehensive error catching and reporting
• Detailed logging through MCP context
• Graceful degradation on rate limits or timeouts

Contributing

Issues and pull requests are welcome! Some areas for potential improvement:

• Additional search parameters (region, language, etc.)
• Enhanced content parsing options
• Caching layer for frequently accessed content
• Additional rate limiting strategies

License

This project is licensed under the MIT License.

Acknowledgments

The code in this project references the following repositories:

• nickclyde/duckduckgo-mcp-server
• BaiduSpider/BaiduSpider

Thanks to the authors and contributors of these repositories for their efforts and contributions to the open-source community.