tavily mcp

Local 2025-08-31 23:23:51 0

This server enables AI systems to integrate with Tavily's search and data extraction tools, providing real-time web information access and domain-specific searches.


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? Compatible with Cline, Cursor, Claude Desktop, and any other MCP Clients!

Tavily MCP is also compatible with any MCP client

? tutorial on combining Tavily MCP with Neo4j MCP server!

? tutorial Integrating Tavily MCP with Cline in VS Code ( Demo + Example Use-Cases)

Tavily MCP Demo

The Model Context Protocol (MCP) is an open standard that enables AI systems to interact seamlessly with various data sources and tools, facilitating secure, two-way connections.

Developed by Anthropic, the Model Context Protocol (MCP) enables AI assistants like Claude to seamlessly integrate with Tavily's advanced search and data extraction capabilities. This integration provides AI models with real-time access to web information, complete with sophisticated filtering options and domain-specific search features.

The Tavily MCP server provides: - Seamless interaction with the tavily-search and tavily-extract tools - Real-time web search capabilities through the tavily-search tool - Intelligent data extraction from web pages via the tavily-extract tool

Prerequisites ?

Before you begin, ensure you have:

  • Tavily API key
  • If you don't have a Tavily API key, you can sign up for a free account here
  • Claude Desktop or Cursor
  • Node.js (v20 or higher)
  • You can verify your Node.js installation by running:
    • node --version
  • Git installed (only needed if using Git installation method)
  • On macOS: brew install git
  • On Linux:
    • Debian/Ubuntu: sudo apt install git
    • RedHat/CentOS: sudo yum install git
  • On Windows: Download Git for Windows

Tavily MCP server installation ⚡

Running with NPX

npx -y [email protected]  

Installing via Smithery

To install Tavily MCP Server for Claude Desktop automatically via Smithery:

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

Although you can launch a server on its own, it's not particularly helpful in isolation. Instead, you should integrate it into an MCP client. Below is an example of how to configure the Claude Desktop app to work with the tavily-mcp server.

Configuring MCP Clients ⚙️

This repository will explain how to configure both Cursor and Claude Desktop to work with the tavily-mcp server.

Configuring Cline ?

The easiest way to set up the Tavily MCP server in Cline is through the marketplace with a single click:

  1. Open Cline in VS Code
  2. Click on the Cline icon in the sidebar
  3. Navigate to the "MCP Servers" tab ( 4 squares )
  4. Search "Tavily" and click "install"
  5. When prompted, enter your Tavily API key

Alternatively, you can manually set up the Tavily MCP server in Cline:

  1. Open the Cline MCP settings file:

### For macOS:

# Using Visual Studio Code
code ~/Library/Application Support/Code/User/globalStorage/saoudrizwan.claude-dev/settings/cline_mcp_settings.json

# Or using TextEdit
open -e ~/Library/Application Support/Code/User/globalStorage/saoudrizwan.claude-dev/settings/cline_mcp_settings.json

### For Windows:

code %APPDATA%CodeUserglobalStoragesaoudrizwan.claude-devsettingscline_mcp_settings.json

  1. Add the Tavily server configuration to the file:

Replace your-api-key-here with your actual Tavily API key.

{
  "mcpServers": {
    "tavily-mcp": {
      "command": "npx",
      "args": ["-y", "[email protected]"],
      "env": {
        "TAVILY_API_KEY": "your-api-key-here"
      },
      "disabled": false,
      "autoApprove": []
    }
  }
}
  1. Save the file and restart Cline if it's already running.

  2. When using Cline, you'll now have access to the Tavily MCP tools. You can ask Cline to use the tavily-search and tavily-extract tools directly in your conversations.

Configuring Cursor ?️

Note: Requires Cursor version 0.45.6 or higher

To set up the Tavily MCP server in Cursor:

  1. Open Cursor Settings
  2. Navigate to Features > MCP Servers
  3. Click on the "+ Add New MCP Server" button
  4. Fill out the following information:
  5. Name: Enter a nickname for the server (e.g., "tavily-mcp")
  6. Type: Select "command" as the type
  7. Command: Enter the command to run the server:
    env TAVILY_API_KEY=your-api-key npx -y [email protected]
    > Important: Replace your-api-key with your Tavily API key. You can get one at app.tavily.com/home

After adding the server, it should appear in the list of MCP servers. You may need to manually press the refresh button in the top right corner of the MCP server to populate the tool list.

The Composer Agent will automatically use the Tavily MCP tools when relevant to your queries. It is better to explicitly request to use the tools by describing what you want to do (e.g., "User tavily-search to search the web for the latest news on AI"). On mac press command + L to open the chat, select the composer option at the top of the screen, beside the submit button select agent and submit the query when ready.

Cursor Interface Example

Configuring the Claude Desktop app ?️

For macOS:

# Create the config file if it doesn't exist
touch "$HOME/Library/Application Support/Claude/claude_desktop_config.json"

# Opens the config file in TextEdit 
open -e "$HOME/Library/Application Support/Claude/claude_desktop_config.json"

# Alternative method using Visual Studio Code (requires VS Code to be installed)
code "$HOME/Library/Application Support/Claude/claude_desktop_config.json"

For Windows:

code %APPDATA%Claudeclaude_desktop_config.json

Add the Tavily server configuration:

Replace your-api-key-here with your actual Tavily API key.

{
  "mcpServers": {
    "tavily-mcp": {
      "command": "npx",
      "args": ["-y", "[email protected]"],
      "env": {
        "TAVILY_API_KEY": "your-api-key-here"
      }
    }
  }
}

2. Git Installation

  1. Clone the repository:

    git clone https://github.com/tavily-ai/tavily-mcp.git
    cd tavily-mcp

  2. Install dependencies:

    npm install

  3. Build the project:

    npm run build

Configuring the Claude Desktop app ⚙️

Follow the configuration steps outlined in the Configuring the Claude Desktop app section above, using the below JSON configuration.

Replace your-api-key-here with your actual Tavily API key and /path/to/tavily-mcp with the actual path where you cloned the repository on your system.

{
  "mcpServers": {
    "tavily": {
      "command": "npx",
      "args": ["/path/to/tavily-mcp/build/index.js"],
      "env": {
        "TAVILY_API_KEY": "your-api-key-here"
      }
    }
  }
}

Usage in Claude Desktop App ?

Once the installation is complete, and the Claude desktop app is configured, you must completely close and re-open the Claude desktop app to see the tavily-mcp server. You should see a hammer icon in the bottom left of the app, indicating available MCP tools, you can click on the hammer icon to see more detial on the tavily-search and tavily-extract tools.

Alt text

Now claude will have complete access to the tavily-mcp server, including the tavily-search and tavily-extract tools. If you insert the below examples into the Claude desktop app, you should see the tavily-mcp server tools in action.

Tavily Search Examples

  1. General Web Search:

    Can you search for recent developments in quantum computing?

  2. News Search:

    Search for news articles about AI startups from the last 7 days.

  3. Domain-Specific Search:

    Search for climate change research on nature.com and sciencedirect.com

Tavily Extract Examples

  1. Extract Article Content:
    Extract the main content from this article: https://example.com/article

✨ Combine Search and Extract ✨

You can also combine the tavily-search and tavily-extract tools to perform more complex tasks.

Search for news articles about AI startups from the last 7 days and extract the main content from each article to generate a detailed report.

Troubleshooting ?️

Common Issues

  1. Server Not Found
  2. Verify the npm installation by running npm --verison
  3. Check Claude Desktop configuration syntax by running code ~/Library/Application Support/Claude/claude_desktop_config.json
  4. Ensure Node.js is properly installed by running node --version

  5. NPX related issues

  6. If you encounter errors related to npx, you may need to use the full path to the npx executable instead.
  7. You can find this path by running which npx in your terminal, then replace the "command": "npx" line with "command": "/full/path/to/npx" in your configuration.

  8. API Key Issues

  9. Confirm your Tavily API key is valid
  10. Check the API key is correctly set in the config
  11. Verify no spaces or quotes around the API key

Acknowledgments ✨

[
  {
    "description": "A powerful web search tool that provides comprehensive, real-time results using Tavily's AI search engine. Returns relevant web content with customizable parameters for result count, content type, and domain filtering. Ideal for gathering current information, news, and detailed web content analysis.",
    "inputSchema": {
      "properties": {
        "days": {
          "default": 3,
          "description": "The number of days back from the current date to include in the search results. This specifies the time frame of data to be retrieved. Please note that this feature is only available when using the 'news' search topic",
          "type": "number"
        },
        "exclude_domains": {
          "default": [],
          "description": "List of domains to specifically exclude, if the user asks to exclude a domain set this to the domain of the site",
          "items": {
            "type": "string"
          },
          "type": "array"
        },
        "include_domains": {
          "default": [],
          "description": "A list of domains to specifically include in the search results, if the user asks to search on specific sites set this to the domain of the site",
          "items": {
            "type": "string"
          },
          "type": "array"
        },
        "include_image_descriptions": {
          "default": false,
          "description": "Include a list of query-related images and their descriptions in the response",
          "type": "boolean"
        },
        "include_images": {
          "default": false,
          "description": "Include a list of query-related images in the response",
          "type": "boolean"
        },
        "include_raw_content": {
          "default": false,
          "description": "Include the cleaned and parsed HTML content of each search result",
          "type": "boolean"
        },
        "max_results": {
          "default": 10,
          "description": "The maximum number of search results to return",
          "maximum": 20,
          "minimum": 5,
          "type": "number"
        },
        "query": {
          "description": "Search query",
          "type": "string"
        },
        "search_depth": {
          "default": "basic",
          "description": "The depth of the search. It can be 'basic' or 'advanced'",
          "enum": [
            "basic",
            "advanced"
          ],
          "type": "string"
        },
        "time_range": {
          "description": "The time range back from the current date to include in the search results. This feature is available for both 'general' and 'news' search topics",
          "enum": [
            "day",
            "week",
            "month",
            "year",
            "d",
            "w",
            "m",
            "y"
          ],
          "type": "string"
        },
        "topic": {
          "default": "general",
          "description": "The category of the search. This will determine which of our agents will be used for the search",
          "enum": [
            "general",
            "news"
          ],
          "type": "string"
        }
      },
      "required": [
        "query"
      ],
      "type": "object"
    },
    "name": "tavily-search"
  },
  {
    "description": "A powerful web content extraction tool that retrieves and processes raw content from specified URLs, ideal for data collection, content analysis, and research tasks.",
    "inputSchema": {
      "properties": {
        "extract_depth": {
          "default": "basic",
          "description": "Depth of extraction - 'basic' or 'advanced', if usrls are linkedin use 'advanced' or if explicitly told to use advanced",
          "enum": [
            "basic",
            "advanced"
          ],
          "type": "string"
        },
        "include_images": {
          "default": false,
          "description": "Include a list of images extracted from the urls in the response",
          "type": "boolean"
        },
        "urls": {
          "description": "List of URLs to extract content from",
          "items": {
            "type": "string"
          },
          "type": "array"
        }
      },
      "required": [
        "urls"
      ],
      "type": "object"
    },
    "name": "tavily-extract"
  }
]