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Yandex Search MCP Server

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Perform real-time web searches using the Yandex Search API.

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Yandex Search MCP Server

This Model Context Protocol (MCP) server lets AI assistants like Claude and Cursor use the Yandex Search API for searching the web. Yandex Search enables LLM agents to safely and easily find up-to-date information on the internet.

Available Tools

The Yandex Search MCP server includes the following tools:

  • ai_search_post Performs a real-time web search and returns an AI-generated answer based on the search results using Yandex Yazeka AI model.
  • web_search_post: Performs a real-time web search and returns answer with sources

Getting API Key

To use this MCP server, you need to be a customer of Yandex Search API. Also you need a Yandex Search API key and folder id:

How to run Yandex Search MCP locally

Prerequisites

  • Python 3.10+
  • Node.js 16+ (for MCP development)
  • Docker 20+ or Podman 3+ (for containerized deployment)

Installation

git clone git@github.com:yandex/yandex-search-mcp-server.git
cd /path/to/yandex-search-mcp-server

Option 1: Use Yandex Search MCP with Docker/Podman

Step 1. Build the container

To run the MCP server in a container using Docker or Podman:

  1. Ensure Docker and Docker Compose, or Podman and Podman Compose, are installed on your system.
  2. For Podman users: If you need to pull images from Docker Hub, first authenticate: 
    podman login docker.io
    Enter your Docker Hub username and password when prompted.
  3. Build the container:
    • Using Docker: 
      docker build -t yandex-mcp-server-image:latest .
    • Using Podman: 
      podman build -t yandex-mcp-server-image:latest .

This will create an image named yandex-mcp-server-image.

Step 2. Add MCP server configs and set SEARCH_API_KEY.

For MCP clients that support direct interaction with Docker or Podman containers, add one of these configurations to the MCP config in your MCP client of choice (e.g. mcp.json in VS Code):

  • Using Docker:
{
  "mcpServers": {
    "yandex-search-api-docker": {
      "autoApprove": [],
      "disabled": true,
      "timeout": 60,
      "type": "stdio",
      "command": "sh",
      "args": [
        "-c",
        "docker rm -f yandex-mcp-container; docker run -i --name yandex-mcp-container --env SEARCH_API_KEY=<your_api_key> --env FOLDER_ID=<your_folder_id> yandex-mcp-server-image:latest"
      ]
    }
  }
}
  • Using Podman:
{
  "mcpServers": {
    "yandex-search-api-podman": {
      "autoApprove": [],
      "disabled": true,
      "timeout": 60,
      "type": "stdio",
      "command": "sh",
      "args": [
        "-c",
        "podman rm -f yandex-mcp-container; podman run -i --name yandex-mcp-container --env SEARCH_API_KEY=<your_api_key> --env FOLDER_ID=<your_folder_id> yandex-mcp-server-image:latest"
      ]
    }
  }
}

Important: Replace <your_api_key> and <your_folder_id> with your actual Yandex Search API key and folder id. Ensure that only one instance of the server (local or Docker) is active at a time to avoid conflicts.

After updating the configuration, the system should automatically detect and run the server, exposing the ai_search_with_yazeka and web_search tools for use.

Option 2: Use Python + MCP Config

Step 1. Install required libraries

  pip install -r requirements.txt

Step 2.1 Configure the MCP Settings in an MCP client

To setup the MCP server on your system using Python, add the following configuration to your MCP settings and set SEARCH_API_KEY:

{
  "mcpServers": {
    "yandex-search-api": {
      "autoApprove": [],
      "disabled": true,
      "timeout": 60,
      "type": "stdio",
      "command": "env",
      "args": [
        "SEARCH_API_KEY=<your_api_key>",
        "FOLDER_ID=<your_folder_id>",
        "python3",
        "/path/to/mcp-server-demo/server.py"
      ]
    }
  }
}

Important: Replace <your_api_key> and <your_folder_id> with your actual Yandex Search API key and folder id. Update /path/to/mcp-server-demo to the actual path where the repository is located on your system if necessary.

After updating the configuration, the system should automatically detect and run the server, exposing the ai_search_with_yazeka and web_search tools for use.

Step 2.2 Launch the local MCP Server directly

To run the MCP server directly on your machine without containerization:

  1. Ensure you have Python 3.10+ installed.
  2. Set the required environment variable for the API key (replace <your_api_key> and <your_folder_id> with your actual Yandex Search API key and folder id):
export SEARCH_API_KEY=<your_api_key>
  1. Run the server:
python3 server.py

The server will start and listen for input on stdin, responding on stdout. Typically, this server is integrated with a system that communicates via MCP, however you can send requests to the server from the terminal.

Example Requests

Copy and Paste these example requests in the terminal with the Yandex Search MCP server running locally to see the search results and the generative AI answer right in the terminal.

Web Search Example

{
    "query": "Who won the most recent Formula 1 race in 2025?",
    "search_region": "en"
}

AI Search Example

{
    "query": "Who won the most recent Formula 1 race in 2025?",
    "search_region": "en"
}