What is Mcp Tavily Search

mcp-tavily-search is a Model Context Protocol (MCP) server that integrates Tavily’s search API with large language models (LLMs) to provide intelligent web search capabilities, optimized for high-quality and factual results.

Use cases

Use cases include generating summaries of search results for quick insights, answering specific questions directly, enhancing RAG applications with contextual data, and filtering domain-specific information for improved relevance.

How to use

To use mcp-tavily-search, configure it through your MCP client by adding the necessary settings, including your Tavily API key. You can set it up for various environments like Cline or Claude Desktop with WSL.

Key features

Key features include advanced web search capabilities, AI-generated summaries, domain filtering, configurable search depth, context generation for RAG applications, direct question answering, response caching, multiple response formats, and structured result formatting optimized for LLMs.

Where to use

mcp-tavily-search can be used in various fields such as research, customer support, content creation, and any application requiring accurate and context-aware search results.

mcp-tavily-search

⚠️ Notice

This repository is no longer maintained.

The functionality of this tool is now available in mcp-omnisearch, which combines multiple MCP tools in one unified package.

Please use mcp-omnisearch instead.

A Model Context Protocol (MCP) server for integrating Tavily’s search
API with LLMs. This server provides intelligent web search
capabilities optimized for high-quality, factual results, including
context generation for RAG applications and direct question answering.

Features

• 🔍 Advanced web search capabilities through Tavily API
• 🤖 AI-generated summaries of search results
• 🎯 Domain filtering for higher quality results
• 📊 Configurable search depth and parameters
• 🧠 Context generation for RAG applications
• ❓ Direct question answering capabilities
• 💾 Response caching with TTL support
• 📝 Multiple response formats (text, JSON, markdown)
• 🔄 Structured result formatting optimized for LLMs
• 🏗️ Built on the Model Context Protocol

Configuration

This server requires configuration through your MCP client. Here are
examples for different environments:

Cline Configuration

Add this to your Cline MCP settings:

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

Claude Desktop with WSL Configuration

For WSL environments, add this to your Claude Desktop configuration:

{
  "mcpServers": {
    "mcp-tavily-search": {
      "command": "wsl.exe",
      "args": [
        "bash",
        "-c",
        "source ~/.nvm/nvm.sh && TAVILY_API_KEY=your-tavily-api-key /home/username/.nvm/versions/node/v20.12.1/bin/npx mcp-tavily-search"
      ]
    }
  }
}

Environment Variables

The server requires the following environment variable:

• TAVILY_API_KEY: Your Tavily API key (required)

API

The server implements three MCP tools with configurable parameters:

tavily_search

Search the web using Tavily Search API, optimized for high-quality,
factual results.

Parameters:

• query (string, required): Search query
• search_depth (string, optional): “basic” (faster) or “advanced”
  (more thorough). Defaults to “basic”
• topic (string, optional): “general” or “news”. Defaults to
  “general”
• days (number, optional): Number of days back to search (news topic
  only). Defaults to 3
• time_range (string, optional): Time range for results (‘day’,
  ‘week’, ‘month’, ‘year’ or ‘d’, ‘w’, ‘m’, ‘y’)
• max_results (number, optional): Maximum number of results.
  Defaults to 5
• include_answer (boolean, optional): Include AI-generated summary.
  Defaults to true
• include_images (boolean, optional): Include related images.
  Defaults to false
• include_image_descriptions (boolean, optional): Include image
  descriptions. Defaults to false
• include_raw_content (boolean, optional): Include raw HTML content.
  Defaults to false
• include_domains (string[], optional): List of trusted domains to
  include
• exclude_domains (string[], optional): List of domains to exclude
• response_format (string, optional): ‘text’, ‘json’, or ‘markdown’.
  Defaults to ‘text’
• cache_ttl (number, optional): Cache time-to-live in seconds.
  Defaults to 3600
• force_refresh (boolean, optional): Force fresh results ignoring
  cache. Defaults to false

tavily_get_search_context

Generate context for RAG applications using Tavily search.

Parameters:

• query (string, required): Search query for context generation
• max_tokens (number, optional): Maximum length of generated
  context. Defaults to 2000
• search_depth (string, optional): “basic” or “advanced”. Defaults
  to “advanced”
• topic (string, optional): “general” or “news”. Defaults to
  “general”
• Other parameters same as tavily_search

tavily_qna_search

Get direct answers to questions using Tavily search.

Parameters:

• query (string, required): Question to be answered
• include_sources (boolean, optional): Include source citations.
  Defaults to true
• search_depth (string, optional): “basic” or “advanced”. Defaults
  to “advanced”
• topic (string, optional): “general” or “news”. Defaults to
  “general”
• Other parameters same as tavily_search

Domain Filtering

The server supports flexible domain filtering through two optional
parameters:

• include_domains: Array of trusted domains to include in search
  results
• exclude_domains: Array of domains to exclude from search results

This allows you to:

• Target specific trusted sources for academic or technical searches
• Exclude potentially unreliable or irrelevant sources
• Customize sources based on your specific needs
• Access all available sources when no filtering is specified

Example domain filtering:

{
  "include_domains": [
    "arxiv.org",
    "science.gov"
  ],
  "exclude_domains": [
    "example.com"
  ]
}

Development

Setup

1. Clone the repository
2. Install dependencies:

pnpm install

3. Build the project:

pnpm build

4. Run in development mode:

pnpm dev

Publishing

The project uses changesets for version management. To publish:

1. Create a changeset:

pnpm changeset

2. Version the package:

pnpm changeset version

3. Publish to npm:

pnpm release

Contributing

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

License

MIT License - see the LICENSE file for details.

Acknowledgments

• Built on the
  Model Context Protocol
• Powered by Tavily Search API