Tavily MCP Server
by RamXX
The Tavily MCP Server provides AI-powered web search capabilities using Tavily's search API. It enables LLMs to perform sophisticated web searches, get direct answers, and search recent news articles with AI-extracted relevant content.
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What is Tavily MCP Server?
The Tavily MCP Server is a Model Context Protocol server that integrates with Tavily's search API to provide AI-powered web search capabilities for Large Language Models (LLMs). It allows LLMs to perform web searches, extract relevant content, and get direct answers to questions.
How to use Tavily MCP Server?
First, install the server using pip, uv, or from source, ensuring you have Python 3.11 or later and a Tavily API key. Configure the API key via a .env file, environment variable, or command-line argument. Then, configure the server within your LLM application (e.g., Claude.app) using the provided JSON configuration. Finally, use the available tools (tavily_web_search, tavily_answer_search, tavily_news_search) with appropriate queries and parameters.
Key features of Tavily MCP Server
AI-powered web search
Direct answer generation with supporting evidence
Recent news article search
AI-extracted relevant content
Domain filtering (include/exclude domains)
Configurable search depth
MCP integration
Use cases of Tavily MCP Server
Enhance LLM responses with up-to-date information
Generate reports with web citations
Answer questions with evidence from the web
Track recent news related to specific topics
Provide LLMs with access to real-time information
FAQ from Tavily MCP Server
How do I get a Tavily API key?
How do I get a Tavily API key?
You can obtain a Tavily API key from Tavily's website (https://tavily.com).
What are the different ways to provide the Tavily API key to the server?
What are the different ways to provide the Tavily API key to the server?
You can provide the API key through a .env file, as an environment variable, or as a command-line argument.
How do I install the server?
How do I install the server?
You can install the server using pip, uv, or by cloning the repository and installing from source.
How do I run the tests?
How do I run the tests?
Install the test dependencies using uv sync --dev or pip install -r requirements-dev.txt, then run the tests using ./tests/run_tests.sh.
How do I debug the server?
How do I debug the server?
You can use the MCP inspector to debug the server using npx @modelcontextprotocol/inspector python -m mcp_server_tavily.