Yandex Search MCP Server

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

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.

The Yandex Search MCP server includes the following tools:

- ai_search_postPerforms 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

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:

- API Key Documentation

https://d5de9siimt9bkld7viic.emzafcgx.apigw.yandexcloud.net:3000/sse

{ "mcpServers": { "yandexSearch": { "command": "npx", "args": [ "-y", "mcp-remote", "https://d5de9siimt9bkld7viic.emzafcgx.apigw.yandexcloud.net:3000/sse", "--header", "ApiKey:<your_api_key>", "--header", "FolderId:<your_folder_id>" ] } } }

- Completely quit Claude Desktop (not just close the window)
- Start Claude Desktop again
- Click "Search and Tools" to verify that yandexSearch is enabled and running

How to Add Yandex Search MCP to Cursor 🤖

1. Configure Cursor to Recognize MCP Servers

Open Settings and navigate to Tools and Integrations in the left panel.

Click the Add Custom MCP button to create the mcp.json config file in cursor

Add the following text to your Claude Desktop configuration file:

{ "mcpServers": { "yandexSearch": { "type": "sse", "url": "https://d5de9siimt9bkld7viic.emzafcgx.apigw.yandexcloud.net:3000/sse", "headers": { "ApiKey": "<your_api_key>", "FolderId": "<your_folder_id>" } } } }

3. Find information on the web with yandexSearch

use yandexSearch to find information about the best AI Agent frameworks in 2025

- Cursor will ask you to confirm that it should use yandexSearch, click the Run Tool button or press (⌘↵) to confirm.
- You should see "called search-api_post" in the chat and the answer based on the web search results.

How to Add Yandex Search MCP to Visual Studio Code 🤖

1. Configure Visual Studio Code to Recognize MCP Servers

Enable the chat.mcp.enabled setting in VS Code to enable MCP support.

Add a.vscode/mcp.json file in your workspace to configure MCP servers for a workspace.

2. Add Yandex Search MCP to Visual Studio Code

Add the following text to your Claude Desktop configuration file:

{ "mcpServers": { "yandexSearch": { "type": "sse", "url": "https://d5de9siimt9bkld7viic.emzafcgx.apigw.yandexcloud.net:3000/sse", "headers": { "ApiKey": "<your_api_key>", "FolderId": "<your_folder_id>" } } } }

- Open the Chat view (⌃⌘I), and select Agent mode from the dropdown.
- Select the Tools button to view the list of available tools.
- The AI Agent will automatically select yandexSearch when it needs to find information on the internet.
- To ensure that the AI Agent uses yandexSearch click the Add Context Button, and select search-api_post from the available tools.
- For advanced configuration settings visitthe official VS Code MCP tutorial

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

podman login docker.io

docker build -t yandex-mcp-server-image:latest .

podman build -t yandex-mcp-server-image:latest .

{ "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" ] } } }

{ "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 theai_search_with_yazekaandweb_searchtools for use.

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 setSEARCH_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-demoto 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 theai_search_with_yazekaandweb_searchtools for use.

Step 2.2 Launch the local MCP Server directly

To run the MCP server directly on your machine without containerization:
- Ensure you have Python 3.10+ installed.
- 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>

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.

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.

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

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

- If the server fails to start, ensure all dependencies are installed correctly (pip install -r requirements.txt).
- If you encounter authentication errors, double-check that theSEARCH_API_KEYis correctly set in your environment or configuration.

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