Baidu Search MCP Server
[](https://smithery.ai/server/@Evilran/baidu-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](https://smithery.ai/server/@Evilran/baidu-mcp-server):
Installing via `uv`
Install directly from PyPI using `uv`:
Usage
Running with Claude Desktop
- Download [Claude Desktop](https://claude.ai/download)
- 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:
- Restart Claude Desktop
Development
For local development, you can use the MCP CLI:
Available Tools
1. Search Tool
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
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](https://github.com/nickclyde/duckduckgo-mcp-server)
- [BaiduSpider/BaiduSpider](https://github.com/BaiduSpider/BaiduSpider)
Thanks to the authors and contributors of these repositories for their efforts and contributions to the open-source community.