Baidu Search
Provides web search capabilities using the Baidu Search API, with features for content fetching and parsing.
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
- Download Claude Desktop
- 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
- On macOS:
Add the following configuration:
{
"mcpServers": {
"baidu-search": {
"command": "uvx",
"args": ["baidu-mcp-server"]
}
}
}
- 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 stringmax_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:
Thanks to the authors and contributors of these repositories for their efforts and contributions to the open-source community.
相關伺服器
Researcher MCP
A research assistant powered by Perplexity AI for intelligent search, documentation retrieval, and code assistance.
Google Search Console MCP Server
Google Search Console MCP Server
SerpApi
Provides search capabilities and data retrieval from SerpAPI and YouTube for AI assistants.
Gemini Grounding Remote
Fetches user data and event information from the Connpass platform using the Connpass and Gemini APIs.
Crossref MCP Server
Search and access academic paper metadata from Crossref.
Semantic API
Natural language API discovery — search 700+ API capabilities, get endpoints, auth setup, and code snippets.
Wizzy TMDB
A wrapper for TMDB
Unified Search MCP Server
Provides unified search capabilities across Google Scholar, Google Web Search, and YouTube.
world airfares flight mcp
Flight search MCP server providing search, pagination, and itinerary details for AI assistants.
Unity Docs
Search and retrieve Unity documentation and API references locally with smart version handling.
