Tavily search

What is mcp-tavily?

mcp-tavily is a Model Context Protocol (MCP) server that integrates with Tavily's AI-powered search API. It allows Large Language Models (LLMs) to perform advanced web searches, retrieve direct answers, and search recent news articles with AI-extracted relevant content.

How to use mcp-tavily?

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

pip install mcp-tavily
# Or with uv (recommended)
uv add mcp-tavily

Option 2: From source

git clone https://github.com/RamXX/mcp-tavily.git
cd mcp-tavily
python -m venv .venv
source .venv/bin/activate
uv sync
uv build

Configuration

Set your Tavily API key via a .env file, environment variable, or command-line argument. For VS Code integration, add the provided JSON configuration to your User Settings or .vscode/mcp.json.

Usage Examples

• Regular web search: "Tell me about Anthropic's newly released MCP protocol"
• Report generation with domain filtering: "Tell me about redwood trees. Please use MLA format in markdown syntax and include the URLs in the citations. Exclude Wikipedia sources."
• Answer search mode: "I want a concrete answer backed by current web sources: What is the average lifespan of redwood trees?"
• News search: "Give me the top 10 AI-related news in the last 5 days"

Key Features of mcp-tavily

Available Tools

• tavily_web_search: Performs comprehensive web searches with AI-powered content extraction. Supports query, max_results, search_depth ("basic" or "advanced"), include_domains, and exclude_domains.
• tavily_answer_search: Performs web searches and generates direct answers with supporting evidence. Similar parameters to tavily_web_search, with a default search_depth of "advanced".
• tavily_news_search: Searches recent news articles with publication dates. Supports query, max_results, days (number of days back to search), include_domains, and exclude_domains.

Prompts

• 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.

Use Cases of mcp-tavily

• Enhancing LLMs with real-time, AI-powered web search capabilities.
• Generating comprehensive reports with specific domain inclusions/exclusions.
• Obtaining direct, evidence-backed answers to specific questions.
• Staying updated with recent news articles on various topics.
• Integrating advanced search functionalities into AI applications and workflows.

FAQ from mcp-tavily

Q: What are the prerequisites for running mcp-tavily? A: You need Python 3.11 or later, a Tavily API key, and uv (recommended) or pip for package management.

Q: How do I provide my Tavily API key? A: You can set it in a .env file, as an environment variable, or pass it as a command-line argument. VS Code integration also allows for easy API key setup.

Q: Can I filter search results by domain? A: Yes, all search tools (tavily_web_search, tavily_answer_search, tavily_news_search) support include_domains and exclude_domains parameters.

Q: How can I debug the mcp-tavily server? A: You can use the MCP inspector by running npx @modelcontextprotocol/inspector python -m mcp_server_tavily.

Q: Does mcp-tavily support news searches? A: Yes, the tavily_news_search tool allows you to search recent news articles and specify the number of days back to search.

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

# With pip
pip install mcp-tavily

# Or with uv (recommended)
uv add mcp-tavily

You should see output similar to:

Resolved packages: mcp-tavily, mcp, pydantic, python-dotenv, tavily-python [...]
Successfully installed mcp-tavily-0.1.4 mcp-1.0.0 [...]

Option 2: From source

# Clone the repository
git clone https://github.com/RamXX/mcp-tavily.git
cd mcp-tavily

# Create a virtual environment (optional but recommended)
python -m venv .venv
source .venv/bin/activate  # On Windows: .venv\Scripts\activate

# Install dependencies and build
uv sync  # Or: pip install -r requirements.txt
uv build  # Or: pip install -e .

# To install with test dependencies:
uv sync --dev  # Or: pip install -r requirements-dev.txt

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.

{
  "mcp": {
    "inputs": [
      {
        "type": "promptString",
        "id": "apiKey",
        "description": "Tavily API Key",
        "password": true
      }
    ],
    "servers": {
      "tavily": {
        "command": "uvx",
        "args": ["mcp-tavily"],
        "env": {
          "TAVILY_API_KEY": "${input:apiKey}"
        }
      }
    }
  }
}

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:

"mcpServers": {
  "tavily": {
    "command": "python",
    "args": ["-m", "mcp_server_tavily"]
  },
  "env": {
    "TAVILY_API_KEY": "your_api_key_here"
  }
}

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:

Tell me about Anthropic's newly released MCP protocol

To generate a report with domain filtering:

Tell me about redwood trees. Please use MLA format in markdown syntax and include the URLs in the citations. Exclude Wikipedia sources.

To use answer search mode for direct answers:

I want a concrete answer backed by current web sources: What is the average lifespan of redwood trees?

For news search:

Give me the top 10 AI-related news in the last 5 days

Testing

The project includes a comprehensive test suite with automated dependency compatibility testing.

Running 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 standard test suite:

   ./tests/run_tests.sh
   # Or using Make
   make test
   

Dependency Compatibility Testing

To ensure the project works with the latest dependency versions, use these commands:

# Test with latest dependencies using Make
make test-deps

# Full compatibility test with verbose output
make test-compatibility

# Or use the standalone script
./scripts/test-compatibility.sh

These commands will:

• Update all dependencies to their latest versions
• Run the full test suite with coverage
• Report any compatibility issues
• Show version changes for transparency

Automated Testing

The project includes automated dependency compatibility testing through GitHub Actions:

• Weekly Testing: Runs every Monday at 8 AM UTC
• Multi-Python Support: Tests against Python 3.11, 3.12, and 3.13
• Issue Creation: Automatically creates GitHub issues when tests fail
• Manual Trigger: Can be triggered manually from the GitHub Actions tab

Understanding Test Results

When tests pass: Your project is compatible with the latest dependency versions. You can safely update your requirements files.

When tests fail: Review the test output to identify breaking changes, update your code to handle API changes, update tests if needed, or consider pinning problematic dependency versions.

Test Output Example

You should see output similar to:

======================================================= test session starts ========================================================
platform darwin -- Python 3.13.3, pytest-8.3.5, pluggy-1.5.0
rootdir: /Users/ramirosalas/workspace/mcp-tavily
configfile: pyproject.toml
plugins: cov-6.0.0, asyncio-0.25.3, anyio-4.8.0, mock-3.14.0
asyncio: mode=Mode.STRICT, asyncio_default_fixture_loop_scope=function
collected 50 items                                                                                                                 

tests/test_docker.py ..                                                                                                      [  4%]
tests/test_integration.py .....                                                                                              [ 14%]
tests/test_models.py .................                                                                                       [ 48%]
tests/test_server_api.py .....................                                                                               [ 90%]
tests/test_utils.py .....                                                                                                    [100%]

---------- coverage: platform darwin, python 3.13.3-final-0 ----------
Name                                Stmts   Miss  Cover
-------------------------------------------------------
src/mcp_server_tavily/__init__.py      16      2    88%
src/mcp_server_tavily/__main__.py       2      2     0%
src/mcp_server_tavily/server.py       149     16    89%
-------------------------------------------------------
TOTAL                                 167     20    88%

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.

Release Management

The project includes tools for building and releasing with the latest dependency versions:

Building with Latest Dependencies

# Build package with latest dependency versions
make build-latest

# Complete release workflow: test, build, and check with latest deps
make release-all

# Prepare a release with version management
./scripts/prepare-release.sh [new_version]

Release Workflow

Recommended approach for releases with latest dependencies:

1. Complete release preparation: make release-all
2. Upload without downgrades: make upload-latest

Alternative step-by-step approach:

1. Test with latest dependencies: make test-compatibility
2. Build for release: make release-build
3. Upload without rebuilding: make upload-latest

One-command release and publish:

make release-publish

Important: Use make upload-latest instead of make upload to prevent dependency downgrades during the upload process. The upload-latest command uses existing distribution files without reinstalling dependencies.

The release commands ensure your package is built and tested with the most recent compatible dependency versions, preventing the downgrades that can occur with traditional build chains.

Docker

Build the Docker image:

make docker-build

Alternatively, build directly with Docker:

docker build -t mcp_tavily .

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

make docker-run

Or manually:

docker run -d --name mcp_tavily_container \
  -e TAVILY_API_KEY=your_api_key_here \
  -p 8000:8000 mcp_tavily

Stop and remove the container:

make docker-stop

Follow container logs:

make docker-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:

# Using npx
npx @modelcontextprotocol/inspector python -m mcp_server_tavily

# For development
cd path/to/mcp-tavily
npx @modelcontextprotocol/inspector python -m mcp_server_tavily

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.