mcp-tavily
mcp server of tavily
Documentation
MCP Tavily
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
}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 claudeManual Installation
npm install @mcptools/mcp-tavilyOr use it directly with npx:
npx @mcptools/mcp-tavilyPrerequisites
- Node.js 16 or higher
- npm or yarn
- Tavily API key (get one from Tavily)
Setup
1. Clone the repository
2. Install dependencies:
npm install3. Set your Tavily API key:
export TAVILY_API_KEY=your_api_keyBuilding
npm run buildDebugging 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
Frequently asked questions
What is mcp-tavily?
mcp-tavily is mcp server of tavily
How do I install mcp-tavily?
Open the GitHub repository and follow its README. Most MCP servers are added to your client's MCP config, then called by your agent.
Is mcp-tavily open source?
Yes — it is hosted on GitHub at https://github.com/kshern/mcp-tavily and has 12 stars.
Related MCP tools
🔥 Official Firecrawl MCP Server - Adds powerful web scraping and search to Cursor, Claude and any other LLM clients.
Use any LLMs (Large Language Models) for Deep Research. Support SSE API and MCP server.
Enhanced MCP server for interactive user feedback and command execution in AI-assisted development, featuring dual interface support (Web UI and Desktop Application) with intelligent environment detection and cross-platform compatibility.
A powerful Zotero AI and MCP plugin with ChatGPT, Gemini 3.7, Claude Fable 5, Claude Opus 5, DeepSeek V4, Grok, OpenRouter, Kimi k3, GLM 5.3, SiliconFlow, GPT-oss, Gemma 4, Qwen 3.8
Connect your browser to AI models. Just use Dia on Chrome, Arc or Firefox.
文颜 MCP Server 可以让 AI 自动将 Markdown 文章排版后发布至微信公众号。
Run your own MCP server? See who uses it and what to fix.
Measure it with TrackMCP