Tavily MCP Server

What does “Tavily” MCP Server do?

The Tavily MCP (Model Context Protocol) Server acts as a bridge between AI assistants and the web, empowering them with advanced real-time search and data extraction functionalities. By leveraging the open MCP standard, Tavily enables seamless and secure integration of its cutting-edge web tools directly into AI development workflows. Through the Tavily MCP server, AI models can perform live web searches, extract structured data from webpages, map website structures, and even crawl entire domains. This dramatically enhances the contextual awareness and real-time capability of AI agents, supporting tasks like information retrieval, research, and knowledge graph building. Tavily MCP server thus acts as a robust platform for connecting AI to external web-based data and resources, unlocking new possibilities for AI-powered automation and intelligent systems.

List of Prompts

No direct prompt templates are mentioned in the provided repository content.

List of Resources

No explicit resources are described in the repository content.

List of Tools

• tavily-search: Provides real-time web search capabilities, allowing AI agents to fetch up-to-date information from the internet.
• tavily-extract: Enables intelligent extraction of structured data from webpages, making it easier to retrieve relevant content and facts.
• tavily-map: Creates a structured map of a website, helping AI systems understand site architecture and relationships between pages.
• tavily-crawl: Systematically explores and crawls websites, collecting data at scale for comprehensive web analysis.

Use Cases of this MCP Server

• Real-time Web Search Integration: Developers can empower AI agents to retrieve the latest information from the web, supporting news aggregation, research, and fact-checking applications.
• Automated Data Extraction: AI systems can extract structured data from various web sources, enabling use cases such as market analysis, lead generation, or academic research.
• Website Mapping and Analysis: Useful for SEO analysis, competitive intelligence, and technical audits by generating structured maps of sites.
• Web Crawling for Knowledge Graphs: Systematic crawling allows developers to build large-scale knowledge graphs or datasets by harvesting information from targeted domains.
• Enhanced Contextual Awareness for AI Agents: By leveraging search and extraction tools, developers can build AI that responds more accurately to user queries with up-to-date web context.

How to set it up

Windsurf

1. Ensure Node.js is installed.
2. Locate your Windsurf configuration file (e.g., windsurf.config.json).
3. Add the Tavily MCP server using the following JSON snippet:

   {
     "mcpServers": {
       "tavily-mcp": {
         "command": "npx",
         "args": ["@tavily-ai/tavily-mcp@latest"]
       }
     }
   }
   

4. Save the file and restart Windsurf.
5. Verify setup by checking if the Tavily MCP tools are available.

Securing API Keys (Windsurf)

{
  "mcpServers": {
    "tavily-mcp": {
      "command": "npx",
      "args": ["@tavily-ai/tavily-mcp@latest"],
      "env": {
        "TAVILY_API_KEY": "${TAVILY_API_KEY}"
      },
      "inputs": {
        "api_key": "${TAVILY_API_KEY}"
      }
    }
  }
}

Store your Tavily API key in an environment variable for enhanced security.

Claude

1. Install Node.js.
2. Open your Claude configuration file.
3. Add the Tavily MCP server configuration:

   {
     "mcpServers": {
       "tavily-mcp": {
         "command": "npx",
         "args": ["@tavily-ai/tavily-mcp@latest"]
       }
     }
   }
   

4. Save changes and restart Claude.
5. Check for Tavily tools in the Claude interface.

Securing API Keys (Claude)

{
  "mcpServers": {
    "tavily-mcp": {
      "command": "npx",
      "args": ["@tavily-ai/tavily-mcp@latest"],
      "env": {
        "TAVILY_API_KEY": "${TAVILY_API_KEY}"
      },
      "inputs": {
        "api_key": "${TAVILY_API_KEY}"
      }
    }
  }
}

Cursor

1. Make sure Node.js is installed on your system.
2. Edit your Cursor configuration file.
3. Insert the following under MCP servers:

   {
     "mcpServers": {
       "tavily-mcp": {
         "command": "npx",
         "args": ["@tavily-ai/tavily-mcp@latest"]
       }
     }
   }
   

4. Save and restart Cursor.
5. Confirm Tavily MCP availability.

Securing API Keys (Cursor)

{
  "mcpServers": {
    "tavily-mcp": {
      "command": "npx",
      "args": ["@tavily-ai/tavily-mcp@latest"],
      "env": {
        "TAVILY_API_KEY": "${TAVILY_API_KEY}"
      },
      "inputs": {
        "api_key": "${TAVILY_API_KEY}"
      }
    }
  }
}

Cline

1. Install Node.js.
2. Find and open your Cline configuration.
3. Add the Tavily MCP server entry:

   {
     "mcpServers": {
       "tavily-mcp": {
         "command": "npx",
         "args": ["@tavily-ai/tavily-mcp@latest"]
       }
     }
   }
   

4. Save your config file and restart Cline.
5. Validate by running a Tavily-related command or tool.

Securing API Keys (Cline)

{
  "mcpServers": {
    "tavily-mcp": {
      "command": "npx",
      "args": ["@tavily-ai/tavily-mcp@latest"],
      "env": {
        "TAVILY_API_KEY": "${TAVILY_API_KEY}"
      },
      "inputs": {
        "api_key": "${TAVILY_API_KEY}"
      }
    }
  }
}

Always store sensitive API keys in environment variables rather than hardcoding them.

How to use this MCP inside flows

Using MCP in FlowHunt

To integrate MCP servers into your FlowHunt workflow, start by adding the MCP component to your flow and connecting it to your AI agent:

Click on the MCP component to open the configuration panel. In the system MCP configuration section, insert your MCP server details using this JSON format:

{
  "tavily-mcp": {
    "transport": "streamable_http",
    "url": "https://yourmcpserver.example/pathtothemcp/url"
  }
}

Once configured, the AI agent is now able to use this MCP as a tool with access to all its functions and capabilities. Remember to change “tavily-mcp” to the actual name of your MCP server and replace the URL with your own MCP server URL.

Overview

Based on the completeness of the documentation and the availability of tools, but with some gaps in resources and prompt templates, I would rate this MCP server’s repository a 7/10 for practical integration and real-world use.

MCP Score