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塔维利搜索MCP服务

一个集成了Tavily搜索API的MCP服务器实现,为大型语言模型提供优化的搜索功能。

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README

Tavily Search MCP Server

smithery badge An MCP server implementation that integrates the Tavily Search API, providing optimized search capabilities for LLMs.

tavily-search-mcp-server MCP server

Features

  • Web Search: Perform web searches optimized for LLMs, with control over search depth, topic, and time range.
  • Content Extraction: Extracts the most relevant content from search results, optimizing for quality and size.
  • Optional Features: Include images, image descriptions, short LLM-generated answers, and raw HTML content.
  • Domain Filtering: Include or exclude specific domains in search results.

Tools

  • tavily_search
    • Execute web searches using the Tavily Search API.
    • Inputs:
      • query (string, required): The search query.
      • search_depth (string, optional): "basic" or "advanced" (default: "basic").
      • topic (string, optional): "general" or "news" (default: "general").
      • days (number, optional): Number of days back for news search (default: 3).
      • time_range (string, optional): Time range filter ("day", "week", "month", "year" or "d", "w", "m", "y").
      • max_results (number, optional): Maximum number of results (default: 5).
      • include_images (boolean, optional): Include related images (default: false).
      • include_image_descriptions (boolean, optional): Include descriptions for images (default: false).
      • include_answer (boolean, optional): Include a short LLM-generated answer (default: false).
      • include_raw_content (boolean, optional): Include raw HTML content (default: false).
      • include_domains (string[], optional): Domains to include.
      • exclude_domains (string[], optional): Domains to exclude.

Setup Guide 🚀

1. Prerequisites

  • Claude Desktop installed on your computer.
  • A Tavily API key: a. Sign up for a Tavily API account. b. Choose a plan (Free tier available). c. Generate your API key from the Tavily dashboard.

2. Installation

  1. Clone this repository somewhere on your computer:

    git clone https://github.com/apappascs/tavily-search-mcp-server.git 
    
  2. Install dependencies & build the project:

    cd tavily-search-mcp-server
    
    npm install
    
    npm run build
    

3. Integration with Claude Desktop

  1. Open your Claude Desktop configuration file:

    # On Mac:
    ~/Library/Application\ Support/Claude/claude_desktop_config.json
    
    # On Windows:
    %APPDATA%\Claude\claude_desktop_config.json
    
  2. Add one of the following to the mcpServers object in your config, depending on whether you want to run the server using npm or docker:

    Option A: Using NPM (stdio transport)

    {
        "mcpServers": {
            "tavily-search-server": {
                "command": "node",
                "args": [
                    "/Users/<username>/<FULL_PATH...>/tavily-search-mcp-server/dist/index.js"
                ],
                "env": {
                    "TAVILY_API_KEY": "your_api_key_here"
                }
            }
        }
    }
    

    Option B: Using NPM (SSE transport)

    {
        "mcpServers": {
            "tavily-search-server": {
                "command": "node",
                "args": [
                    "/Users/<username>/<FULL_PATH...>/tavily-search-mcp-server/dist/sse.js"
                ],
                "env": {
                    "TAVILY_API_KEY": "your_api_key_here"
                },
                "port": 3001
            }
        }
    }
    

    Option C: Using Docker

    {
        "mcpServers": {
            "tavily-search-server": {
                "command": "docker",
                "args": [
                    "run",
                    "-i",
                    "--rm",
                    "-e",
                    "TAVILY_API_KEY",
                    "-v",
                    "/Users/<username>/<FULL_PATH...>/tavily-search-mcp-server:/app",
                    "tavily-search-mcp-server"
                ],
                "env": {
                    "TAVILY_API_KEY": "your_api_key_here"
                }
            }
        }
    }
    
  3. Important Steps:

    • Replace /Users/<username>/<FULL_PATH...>/tavily-search-mcp-server with the actual full path to where you cloned the repository.
    • Add your Tavily API key in the env section. It's always better to have secrets like API keys as environment variables.
    • Make sure to use forward slashes (/) in the path, even on Windows.
    • If you are using docker make sure you build the image first using docker build -t tavily-search-mcp-server:latest .
  4. Restart Claude Desktop for the changes to take effect.

Installing via Smithery

To install Tavily Search for Claude Desktop automatically via Smithery:

npx -y @smithery/cli install @apappascs/tavily-search-mcp-server --client claude

Environment Setup (for npm)

  1. Copy .env.example to .env:

    cp .env.example .env
    
  2. Update the .env file with your actual Tavily API key:

    TAVILY_API_KEY=your_api_key_here
    

    Note: Never commit your actual API key to version control. The .env file is ignored by git for security reasons.

Running with NPM

Start the server using Node.js:

node dist/index.js

For sse transport:

node dist/sse.js

Running with Docker

  1. Build the Docker image (if you haven't already):

    docker build -t tavily-search-mcp-server:latest .
    
  2. Run the Docker container with:

    For stdio transport:

    docker run -it --rm -e TAVILY_API_KEY="your_api_key_here" tavily-search-mcp-server:latest
    

    For sse transport:

    docker run -it --rm -p 3001:3001 -e TAVILY_API_KEY="your_api_key_here" -e TRANSPORT="sse" tavily-search-mcp-server:latest
    

    You can also leverage your shell's environment variables directly, which is a more secure practice:

     docker run -it --rm -p 3001:3001 -e TAVILY_API_KEY=$TAVILY_API_KEY -e TRANSPORT="sse" tavily-search-mcp-server:latest
    

    Note: The second command demonstrates the recommended approach of using -e TAVILY_API_KEY=$TAVILY_API_KEY to pass the value of your TAVILY_API_KEY environment variable into the Docker container. This keeps your API key out of your command history, and it is generally preferred over hardcoding secrets in commands.

  3. Using docker compose

    Run:

    docker compose up -d
    

    To stop the server:

    docker compose down
    

License

This MCP server is licensed under the MIT License. This means you are free to use, modify, and distribute the software, subject to the terms and conditions of the MIT License. For more details, please see the LICENSE file in the project repository.

help

运行方式说明

cloud

托管运行

托管运行通常表示这个 MCP Server 由服务方环境承载,用户一般按页面提供的连接方式或授权流程接入,不需要在本地长期启动一个 MCP 进程

  1. 打开服务方连接页
  2. 完成授权或复制端点
  3. 在 MCP 客户端中连接
terminal

本地运行 / 其它方式

本地运行通常需要用户在自己的电脑或服务器上安装依赖,把 server_config 复制到 MCP 客户端,并按 env_schema 补齐环境变量、密钥或其它配置

  1. 复制 server_config
  2. 安装所需依赖
  3. 补齐环境变量后重启客户端