mcp-tavily

WHAT IT CONNECTS

What mcp-tavily gives an AI Agent

• `tavily_web_search` - Performs comprehensive web searches with AI-powered content extraction.
• `query` (string, required): Search query
• `max_results` (integer, optional): Maximum number of results to return (default: 5, max: 20)
• `search_depth` (string, optional): Either "basic" or "advanced" search depth (default: "basic")
• `include_domains` (list or string, optional): List of domains to specifically include in results
• `exclude_domains` (list or string, optional): List of domains to exclude from results
• `tavily_answer_search` - Performs web searches and generates direct answers with supporting evidence.
• `query` (string, required): Search query
• `max_results` (integer, optional): Maximum number of results to return (default: 5, max: 20)
• `search_depth` (string, optional): Either "basic" or "advanced" search depth (default: "advanced")
• `include_domains` (list or string, optional): List of domains to specifically include in results
• `exclude_domains` (list or string, optional): List of domains to exclude from results
• `tavily_news_search` - Searches recent news articles with publication dates.
• `query` (string, required): Search query
• `max_results` (integer, optional): Maximum number of results to return (default: 5, max: 20)
• `days` (integer, optional): Number of days back to search (default: 3)
• `include_domains` (list or string, optional): List of domains to specifically include in results
• `exclude_domains` (list or string, optional): List of domains to exclude from results
• **tavily_web_search** - Search the web using Tavily's AI-powered search engine
• **tavily_answer_search** - Search the web and get an AI-generated answer with supporting evidence
• **tavily_news_search** - Search recent news articles with Tavily's news search
• Python 3.11 or later
• A Tavily API key (obtain from [Tavily's website](https://tavily.com))
• `uv` Python package manager (recommended)
• Through a `.env` file in your project directory:
• As an environment variable:
• As a command-line argument:
• Install test dependencies:
• Run the tests:
• DOCKER_IMAGE: image name (default `mcp_tavily`)
• DOCKER_CONTAINER: container name (default `mcp_tavily_container`)
• HOST_PORT: host port to bind (default `8000`)
• CONTAINER_PORT: container port (default `8000`)
• Fork the repository
• Create a feature branch (`git checkout -b feature/amazing-feature`)
• Make your changes
• Run tests to ensure they pass
• Commit your changes (`git commit -m 'Add amazing feature'`)
• Push to the branch (`git push origin feature/amazing-feature`)
• Open a Pull Request