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
pip install mcp-tavily
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
git clone https://github.com/RamXX/mcp-tavily.git
cd mcp-tavily
python -m venv .venv
source .venv/bin/activate
uv sync
uv build
uv sync --dev
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:
-
Through a .env
file in your project directory:
TAVILY_API_KEY=your_api_key_here
-
As an environment variable:
export TAVILY_API_KEY=your_api_key_here
-
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
-
Install test dependencies:
source .venv/bin/activate
uv sync --dev
-
Run the standard test suite:
./tests/run_tests.sh
make test
Dependency Compatibility Testing
To ensure the project works with the latest dependency versions, use these commands:
make test-deps
make test-compatibility
./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
make build-latest
make release-all
./scripts/prepare-release.sh [new_version]
Release Workflow
Recommended approach for releases with latest dependencies:
- Complete release preparation:
make release-all
- Upload without downgrades:
make upload-latest
Alternative step-by-step approach:
- Test with latest dependencies:
make test-compatibility
- Build for release:
make release-build
- 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:
npx @modelcontextprotocol/inspector python -m mcp_server_tavily
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:
- 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
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.