Tavily MCP Server

Tavily MCP Server

A Model Context Protocol server that provides AI-powered web search capabilities using Tavily's search API. This server enables LLMs to perform sophisticated web searches, get direct answers to questions, and search recent news articles with AI-extracted relevant content.

Features

Available Tools

• 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

Prompts

The server also provides prompt templates for each search type:

• 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

Prerequisites

• Python 3.11 or later
• A Tavily API key (obtain from Tavily's website)
• uv Python package manager (recommended)

Installation

Option 1: Using pip or uv

You should see output similar to:

Option 2: From source

During installation, you should see the package being built and installed with its dependencies.

Usage with VS Code

For quick installation, use one of the one-click install buttons below:

For manual installation, add the following JSON block to your User Settings (JSON) file in VS Code. You can do this by pressing Ctrl + Shift + P and typing Preferences: Open User Settings (JSON).

Optionally, you can add it to a file called .vscode/mcp.json in your workspace. This will allow you to share the configuration with others.

Note that the mcp key is not needed in the .vscode/mcp.json file.

Configuration

API Key Setup

The server requires a Tavily API key, which can be provided in three ways:

1. Through a .env file in your project directory:
   TAVILY_API_KEY=your_api_key_here
2. As an environment variable:
   export TAVILY_API_KEY=your_api_key_here
3. As a command-line argument:
   python -m mcp_server_tavily --api-key=your_api_key_here

Configure for Claude.app

Add to your Claude settings:

If you encounter issues, you may need to specify the full path to your Python interpreter. Run which python to find the exact path.

Usage Examples

For a regular web search:

To generate a report with domain filtering:

To use answer search mode for direct answers:

For news search:

Testing

The project includes a comprehensive test suite. To run the tests:

1. Install test dependencies:
   source .venv/bin/activate # If using a virtual environment uv sync --dev # Or: pip install -r requirements-dev.txt
2. Run the tests:
   ./tests/run_tests.sh

You should see output similar to:

The test suite includes tests for data models, utility functions, integration testing, error handling, and parameter validation. It focuses on verifying that all API capabilities work correctly, including handling of domain filters and various input formats.

Docker

Build the Docker image:

Alternatively, build directly with Docker:

Run a detached Docker container (default name mcp_tavily_container, port 8000 → 8000):

Or manually:

Stop and remove the container:

Follow container logs:

You can override defaults by setting environment variables:

• 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)

Debugging

You can use the MCP inspector to debug the server:

Contributing

We welcome contributions to improve mcp-tavily! Here's how you can help:

1. Fork the repository
2. Create a feature branch (git checkout -b feature/amazing-feature)
3. Make your changes
4. Run tests to ensure they pass
5. Commit your changes (git commit -m 'Add amazing feature')
6. Push to the branch (git push origin feature/amazing-feature)
7. Open a Pull Request

For examples of other MCP servers and implementation patterns, see: https://github.com/modelcontextprotocol/servers

License

mcp-tavily is licensed under the MIT License. See the LICENSE file for details.