Tavily Search MCP Agent
š My Tavily Search MCP Agent
I've created a powerful Model Context Protocol (MCP) Server powered by the Tavily API. With this, you can get high-quality, reliable information from business, news, finance, and politics - all through a robust and developer-friendly interface.
š Why I Built Tavily Search MCP
In today's fast-paced digital landscape, I recognized the need for quick access to precise information. I needed a web search tool that works with my sequential thinking MCP server. That's why I developed Tavily Search MCP, which excels with:
ā”ļø Lightning-fast async search responses
š”ļø Built-in fault tolerance with automatic retries
šÆ Clean, markdown-formatted results
š Smart content snippets
š ļø Comprehensive error handling
š¼ļø Optional image results
š° Specialized news search
š Quick Start
Installing via Smithery
To install Tavily Search for Claude Desktop automatically via Smithery:
Installing Manually
Here's how you can get up and running with my project in minutes:
š” Core Features
ā”ļø Performance & Reliability
- I've implemented asynchronous request handling
- Built-in error handling and automatic retries
- Configurable request timeouts
- Comprehensive logging system
šÆ Search Configuration
- I've made the search depth configurable (basic/advanced)
- Adjustable result limits (1-20 results)
- Clean markdown-formatted output
- Snippet previews with source URLs
- Optional image results
- Specialized news search topic
š”ļø Error Handling
- API authentication validation
- Rate limit detection
- Network error recovery
- Request timeout management
š ļø Developer Integration
Prerequisites
- Python 3.11 or higher
- UV Package Manager (Installation Guide)
- Tavily API key (Get one here)
Claude Desktop Setup
I've optimized the Claude Desktop experience with this configuration:
š Configuration paths:
- Windows:
%APPDATA%\Claude\claude_desktop_config.json
- Unix/MacOS:
~/.config/Claude/claude_desktop_config.json
Project Architecture
I've designed a clean, modular structure to make development a breeze:
Key Components
Server (server.py)
- I've implemented the MCP protocol
- Request handling and routing
- Error recovery and health monitoring
Client (client.py)
- Tavily API integration
- Retry mechanism with exponential backoff
- Result formatting and processing
- Error handling and logging
Tests (test_server.py and test_client.py)
- Comprehensive unit tests for both server and client
- Ensures reliability and correctness of the implementation
Usage Examples
Here are some examples of how to use the enhanced search capabilities I've implemented:
- Basic search:
- Advanced search with images:
- News-specific search:
- Search with raw content:
Troubleshooting Guide
Connection Issues
If things don't work as expected, follow these steps I've outlined:
- Verify your configuration paths
- Check the Claude Desktop logs:Copy
- Test the server manually using the quick start commands
API Troubleshooting
If you're experiencing API issues:
- Validate your API key permissions
- Check your network connection
- Monitor the API response in the server logs
Running Tests
To run the unit tests for this project, follow these steps:
- Install the development dependencies:Copy
- Run the tests using pytest:Copy
This will run all the tests in the mcp_tavily_search
directory, including both test_client.py
and test_server.py
.
Community and Support
- I encourage you to report issues and contribute on GitHub
- Share your implementations and improvements
- Join our discussions and help others
Security and Best Practices
Security is paramount in my implementation. The server includes:
- Secure API key handling through environment variables
- Automatic request timeout management
- Comprehensive error tracking and logging
License
I've licensed this project under MIT. See the LICENSE file for details.
Acknowledgments
I'd like to give special thanks to:
- The innovative Tavily API team
- The MCP protocol community
You must be authenticated.
This MCP server performs multi-topic searches in business, news, finance, and politics using the Tavily API, providing high-quality sources and intelligent summaries.