What is Mcp Tavily

mcp-tavily is an implementation of a Model Context Protocol (MCP) server specifically designed for the Tavily API, providing advanced search and content extraction capabilities.

Use cases

Use cases for mcp-tavily include academic research for finding relevant literature, customer support for answering user queries, and content curation for extracting and summarizing information from multiple sources.

How to use

To use mcp-tavily, add it to your MCP configuration by specifying the command and arguments in JSON format, including your Tavily API key as an environment variable or directly in the configuration.

Key features

Key features of mcp-tavily include multiple search tools (basic search, context-aware search, and Q&A search), content extraction from URLs, and rich configuration options for search depth and filtering.

Where to use

mcp-tavily can be used in various fields such as information retrieval, content management, and data extraction, where advanced search capabilities are required.

MCP Tavily

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A Model Context Protocol (MCP) server implementation for Tavily API, providing advanced search and content extraction capabilities.

Features

• Multiple Search Tools:
  • search: Basic search functionality with customizable options
  • searchContext: Context-aware search for better relevance
  • searchQNA: Question and answer focused search
• Content Extraction: Extract content from URLs with configurable options
• Rich Configuration Options: Extensive options for search depth, filtering, and content inclusion

Usage with MCP

Add the Tavily MCP server to your MCP configuration:

{
  "mcpServers": {
    "tavily": {
      "command": "npx",
      "args": [
        "-y",
        "@mcptools/mcp-tavily"
      ],
      "env": {
        "TAVILY_API_KEY": "your-api-key"
      }
    }
  }
}

Note: Make sure to replace your-api-key with your actual Tavily API key. You can also set it as an environment variable TAVILY_API_KEY before running the server.

API Reference

Search Tools

The server provides three search tools that can be called through MCP:

1. Basic Search

// Tool name: search
{
  query: "artificial intelligence",
  options: {
    searchDepth: "advanced",
    topic: "news",
    maxResults: 10
  }
}

2. Context Search

// Tool name: searchContext
{
  query: "latest developments in AI",
  options: {
    topic: "news",
    timeRange: "week"
  }
}

3. Q&A Search

// Tool name: searchQNA
{
  query: "What is quantum computing?",
  options: {
    includeAnswer: true,
    maxResults: 5
  }
}

Extract Tool

// Tool name: extract
{
  urls: ["https://example.com/article1", "https://example.com/article2"],
  options: {
    extractDepth: "advanced",
    includeImages: true
  }
}

Search Options

All search tools share these options:

interface SearchOptions {
  searchDepth?: "basic" | "advanced";    // Search depth level
  topic?: "general" | "news" | "finance"; // Search topic category
  days?: number;                         // Number of days to search
  maxResults?: number;                   // Maximum number of results
  includeImages?: boolean;               // Include images in results
  includeImageDescriptions?: boolean;    // Include image descriptions
  includeAnswer?: boolean;               // Include answer in results
  includeRawContent?: boolean;           // Include raw content
  includeDomains?: string[];            // List of domains to include
  excludeDomains?: string[];            // List of domains to exclude
  maxTokens?: number;                    // Maximum number of tokens
  timeRange?: "year" | "month" | "week" | "day" | "y" | "m" | "w" | "d"; // Time range for search
}

Extract Options

interface ExtractOptions {
  extractDepth?: "basic" | "advanced";   // Extraction depth level
  includeImages?: boolean;               // Include images in results
}

Response Format

All tools return responses in the following format:

{
  content: Array<{
    type: "text",
    text: string
  }>
}

For search results, each item includes:

• Title
• Content
• URL

For extracted content, each item includes:

• URL
• Raw content
• Failed URLs list (if any)

Error Handling

All tools include proper error handling and will throw descriptive error messages if something goes wrong.

Installation

Installing via Smithery

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

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

Manual Installation

npm install @mcptools/mcp-tavily

Or use it directly with npx:

npx @mcptools/mcp-tavily

Prerequisites

• Node.js 16 or higher
• npm or yarn
• Tavily API key (get one from Tavily)

Setup

1. Clone the repository
2. Install dependencies:

npm install

3. Set your Tavily API key:

export TAVILY_API_KEY=your_api_key

Building

npm run build

Debugging with MCP Inspector

For development and debugging, we recommend using MCP Inspector, a powerful development tool for MCP servers.

The Inspector provides a user interface for:

• Testing tool calls
• Viewing server responses
• Debugging tool execution
• Monitoring server state

Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

1. Fork the repository
2. Create your feature branch (git checkout -b feature/AmazingFeature)
3. Commit your changes (git commit -m 'Add some AmazingFeature')
4. Push to the branch (git push origin feature/AmazingFeature)
5. Open a Pull Request

License

This project is licensed under the MIT License.

Support

For any questions or issues:

• Tavily API: refer to the Tavily documentation
• MCP integration: refer to the MCP documentation