Tavily Search

by arben-adm

1 Not rated yet MIT

GitHub

About

Integrates with Tavily search API to enable natural language web queries for retrieving up-to-date, contextual information from across the internet.

Details

Repository: arben-adm/tavily-mcp-search
License: MIT

Explore

Setting up with Highlight

This MCP is not yet compatible with Highlight’s one-click setup. However, you can still use it with Highlight by following these steps:

Download and install Highlight from highlightai.com/download
Navigate to the plugins tab and select "Add Custom Plugin"
Configure the plugin with the settings below
Plugin Name Tavily Search

Command (node, npx, python, etc.) uv
Arguments
- Argument 1 --directory
- Argument 2 /path/to/mcp-tavily-search/mcp_tavily_search
- Argument 3 run
- Argument 4 server.py
Environment
- TAVILY_API_KEY YOUR-API-KEY
Please refer to the README for specific instructions on how to obtain API keys or other required environment variables.
Enable "Start Automatically" if you want the plugin to start when Highlight launches

Setting up with Cursor

Open Cursor's settings
Navigate to the MCP section
Click "Add new Global MCP server"
Copy and paste the configuration below based on your operating system

JSON

{
    "mcpServers": {
        "tavily search": {
            "env": {
                "TAVILY_API_KEY": "YOUR-API-KEY"
            },
            "args": [
                "--directory",
                "/path/to/mcp-tavily-search/mcp_tavily_search",
                "run",
                "server.py"
            ],
            "command": "uv"
        }
    }
}

JSON

{
    "mcpServers": {
        "tavily search": {
            "env": {
                "TAVILY_API_KEY": "YOUR-API-KEY"
            },
            "args": [
                "--directory",
                "/path/to/mcp-tavily-search/mcp_tavily_search",
                "run",
                "server.py"
            ],
            "command": "uv"
        }
    }
}

JSON

{
    "mcpServers": {
        "tavily search": {
            "env": {
                "TAVILY_API_KEY": "YOUR-API-KEY"
            },
            "args": [
                "--directory",
                "/path/to/mcp-tavily-search/mcp_tavily_search",
                "run",
                "server.py"
            ],
            "command": "uv"
        }
    }
}

Setting up with Claude Desktop

Open Claude Desktop's settings
Go to the Integrations tab
Click "Add MCP Server"
Use the configuration below based on your operating system

Note: Make sure Claude Desktop is running before attempting to connect.

JSON

{
    "mcpServers": {
        "tavily search": {
            "env": {
                "TAVILY_API_KEY": "YOUR-API-KEY"
            },
            "args": [
                "--directory",
                "/path/to/mcp-tavily-search/mcp_tavily_search",
                "run",
                "server.py"
            ],
            "command": "uv"
        }
    }
}

JSON

{
    "mcpServers": {
        "tavily search": {
            "env": {
                "TAVILY_API_KEY": "YOUR-API-KEY"
            },
            "args": [
                "--directory",
                "/path/to/mcp-tavily-search/mcp_tavily_search",
                "run",
                "server.py"
            ],
            "command": "uv"
        }
    }
}

JSON

{
    "mcpServers": {
        "tavily search": {
            "env": {
                "TAVILY_API_KEY": "YOUR-API-KEY"
            },
            "args": [
                "--directory",
                "/path/to/mcp-tavily-search/mcp_tavily_search",
                "run",
                "server.py"
            ],
            "command": "uv"
        }
    }
}

Setting up with Windsurf

Open Windsurf's preferences
Go to the Integrations section
Click "Add MCP Integration"
Use the configuration below based on your operating system

JSON

{
    "mcpServers": {
        "tavily search": {
            "env": {
                "TAVILY_API_KEY": "YOUR-API-KEY"
            },
            "args": [
                "--directory",
                "/path/to/mcp-tavily-search/mcp_tavily_search",
                "run",
                "server.py"
            ],
            "command": "uv"
        }
    }
}

JSON

{
    "mcpServers": {
        "tavily search": {
            "env": {
                "TAVILY_API_KEY": "YOUR-API-KEY"
            },
            "args": [
                "--directory",
                "/path/to/mcp-tavily-search/mcp_tavily_search",
                "run",
                "server.py"
            ],
            "command": "uv"
        }
    }
}

JSON

{
    "mcpServers": {
        "tavily search": {
            "env": {
                "TAVILY_API_KEY": "YOUR-API-KEY"
            },
            "args": [
                "--directory",
                "/path/to/mcp-tavily-search/mcp_tavily_search",
                "run",
                "server.py"
            ],
            "command": "uv"
        }
    }
}

Setting up with Cline

Open Cline's settings
Navigate to the Integrations section
Click "Add MCP Server"
Copy and paste the configuration below based on your operating system

Note: Make sure to restart Cline after adding the configuration.

JSON

{
    "mcpServers": {
        "tavily search": {
            "env": {
                "TAVILY_API_KEY": "YOUR-API-KEY"
            },
            "args": [
                "--directory",
                "/path/to/mcp-tavily-search/mcp_tavily_search",
                "run",
                "server.py"
            ],
            "command": "uv"
        }
    }
}

JSON

{
    "mcpServers": {
        "tavily search": {
            "env": {
                "TAVILY_API_KEY": "YOUR-API-KEY"
            },
            "args": [
                "--directory",
                "/path/to/mcp-tavily-search/mcp_tavily_search",
                "run",
                "server.py"
            ],
            "command": "uv"
        }
    }
}

JSON

{
    "mcpServers": {
        "tavily search": {
            "env": {
                "TAVILY_API_KEY": "YOUR-API-KEY"
            },
            "args": [
                "--directory",
                "/path/to/mcp-tavily-search/mcp_tavily_search",
                "run",
                "server.py"
            ],
            "command": "uv"
        }
    }
}

Setting up with Goose

Open Goose's preferences
Go to the Integrations tab
Select "MCP Servers"
Click "Add New Server"
Use the configuration below based on your operating system

JSON

{
    "mcpServers": {
        "tavily search": {
            "env": {
                "TAVILY_API_KEY": "YOUR-API-KEY"
            },
            "args": [
                "--directory",
                "/path/to/mcp-tavily-search/mcp_tavily_search",
                "run",
                "server.py"
            ],
            "command": "uv"
        }
    }
}

JSON

{
    "mcpServers": {
        "tavily search": {
            "env": {
                "TAVILY_API_KEY": "YOUR-API-KEY"
            },
            "args": [
                "--directory",
                "/path/to/mcp-tavily-search/mcp_tavily_search",
                "run",
                "server.py"
            ],
            "command": "uv"
        }
    }
}

JSON

{
    "mcpServers": {
        "tavily search": {
            "env": {
                "TAVILY_API_KEY": "YOUR-API-KEY"
            },
            "args": [
                "--directory",
                "/path/to/mcp-tavily-search/mcp_tavily_search",
                "run",
                "server.py"
            ],
            "command": "uv"
        }
    }
}

From the repository

- Python 3.11 or higher
- UV Package Manager (Installation Guide)
- Tavily API key (Get one here)

To install Tavily Search for Claude Desktop automatically via Smithery:

npx -y @smithery/cli install mcp-tavily-search --client claude

Here's how you can get up and running with my project in minutes:

uv venv && .venv\Scripts\activate # Windows uv pip install -e . echo TAVILY_API_KEY=your-key-here > .env - I've made the search depth configurable (basic/advanced) - Adjustable result limits (1-20 results) - Clean markdown-formatted output - Snippet previews with source URLs - Optional image results - Specialized news search topic I've optimized the Claude Desktop experience with this configuration:

json
{
"mcpServers": {
"tavily-search": {
"command": "uv",
"args": [
"--directory",
"/path/to/mcp-tavily-search/mcp_tavily_search",
"run",
"server.py"
],
"env": {
"TAVILY_API_KEY": "YOUR-API-KEY"
}
}
}
}

📁 Configuration paths: - Windows: %APPDATA%\Claude\claude_desktop_config.json - Unix/MacOS: ~/.config/Claude/claude_desktop_config.json Here are some examples of how to use the enhanced search capabilities I've implemented:

1. Basic search:

json
{
"name": "search",
"arguments": {
"query": "Latest news on artificial intelligence"
}
}



2. Advanced search with images:

json
{
"name": "search",
"arguments": {
"query": "Elon Musk SpaceX achievements",
"search_depth": "advanced",
"include_images": true,
"max_results": 10
}
}



3. News-specific search:

json
{
"name": "search",
"arguments": {
"query": "Climate change impact on agriculture",
"topic": "news",
"max_results": 5
}
}



4. Search with raw content:

json
{
"name": "search",
"arguments": {
"query": "Python programming best practices",
"include_raw_content": true,
"max_results": 3
}
}

To run the unit tests for this project, follow these steps:

1. Install the development dependencies:

bash
uv pip install -e ".[dev]"



2. Run the tests using pytest:

bash
pytest mcp_tavily_search
``

This will run all the tests in the mcp_tavily_search directory, including both test_client.py and test_server.py`.

`search`

Perform a search query. Parameters: query (string), search_depth (optional string), include_images (optional boolean), max_results (optional integer), topic (optional string), include_raw_content (optional boolean)

Claude Desktop / Cursor

Paste into your MCP client config file to install this server.

{
    "mcpServers": {
        "tavily search": {
            "env": {
                "TAVILY_API_KEY": "YOUR-API-KEY"
            },
            "args": [
                "--directory",
                "/path/to/mcp-tavily-search/mcp_tavily_search",
                "run",
                "server.py"
            ],
            "command": "uv"
        }
    }
}

Linux

{
    "env": {
        "TAVILY_API_KEY": "YOUR-API-KEY"
    },
    "args": [
        "--directory",
        "/path/to/mcp-tavily-search/mcp_tavily_search",
        "run",
        "server.py"
    ],
    "command": "uv"
}

Macos

{
    "env": {
        "TAVILY_API_KEY": "YOUR-API-KEY"
    },
    "args": [
        "--directory",
        "/path/to/mcp-tavily-search/mcp_tavily_search",
        "run",
        "server.py"
    ],
    "command": "uv"
}

Windows

{
    "env": {
        "TAVILY_API_KEY": "YOUR-API-KEY"
    },
    "args": [
        "--directory",
        "/path/to/mcp-tavily-search/mcp_tavily_search",
        "run",
        "server.py"
    ],
    "command": "uv"
}

🔍 My Tavily Search MCP Agent

I've created a powerful Model Context Protocol (MCP) Server powered by the Tavily API. With this, you can get high-quality, reliable information from business, news, finance, and politics - all through a robust and developer-friendly interface.

🌟 Why I Built Tavily Search MCP

In today's fast-paced digital landscape, I recognized the need for quick access to precise information. I needed a web search tool that works with my sequential thinking MCP server. That's why I developed Tavily Search MCP, which excels with:

⚡️ Lightning-fast async search responses
🛡️ Built-in fault tolerance with automatic retries
🎯 Clean, markdown-formatted results
🔍 Smart content snippets
🛠️ Comprehensive error handling
🖼️ Optional image results
📰 Specialized news search

🚀 Quick Start

Installing via Smithery

To install Tavily Search for Claude Desktop automatically via Smithery:

npx -y @smithery/cli install mcp-tavily-search --client claude

Installing Manually

Here's how you can get up and running with my project in minutes:

```bash

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About

Details

Explore

Setting up with Highlight

From the repository

search

Claude Desktop / Cursor

Linux

Macos

Windows

🔍 My Tavily Search MCP Agent

🌟 Why I Built Tavily Search MCP

🚀 Quick Start

Installing via Smithery

Installing Manually

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