MCP Tavily Search Server
mcp-tavily-search
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.
<a href="https://glama.ai/mcp/servers/1jcttrux58"><img width="380" height="200" src="https://glama.ai/mcp/servers/1jcttrux58/badge" alt="Tavily Search Server MCP server" /></a>
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:
Claude Desktop with WSL Configuration
For WSL environments, add this to your Claude Desktop configuration:
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 querysearch_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 3time_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 5include_answer
(boolean, optional): Include AI-generated summary. Defaults to trueinclude_images
(boolean, optional): Include related images. Defaults to falseinclude_image_descriptions
(boolean, optional): Include image descriptions. Defaults to falseinclude_raw_content
(boolean, optional): Include raw HTML content. Defaults to falseinclude_domains
(string[], optional): List of trusted domains to includeexclude_domains
(string[], optional): List of domains to excluderesponse_format
(string, optional): 'text', 'json', or 'markdown'. Defaults to 'text'cache_ttl
(number, optional): Cache time-to-live in seconds. Defaults to 3600force_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 generationmax_tokens
(number, optional): Maximum length of generated context. Defaults to 2000search_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 answeredinclude_sources
(boolean, optional): Include source citations. Defaults to truesearch_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 resultsexclude_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:
Development
Setup
- Clone the repository
- Install dependencies:
- Build the project:
- Run in development mode:
Publishing
The project uses changesets for version management. To publish:
- Create a changeset:
- Version the package:
- Publish to npm:
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
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Integrates Tavily's search API with LLMs to provide advanced web search capabilities, including intelligent result summaries, domain filtering for quality control, and configurable search parameters.