Yandex Search MCP Server

The Yandex Search MCP Server acts as a powerful set of "eyes" for AI assistants, allowing models like Claude or Cursor to browse the live web. Instead of relying solely on their training data, which might be outdated, this tool gives AI agents the ability to look up real-time information, news, and facts across the internet. It bridges the gap between static AI knowledge and the ever-changing digital world, ensuring that the answers provided are as current as possible. Under the hood, this server provides two primary tools that developers can leverage depending on the specific needs of their agent. The `web_search_post` tool delivers traditional search results complete with source links, which is perfect for verification and deep research. For more immediate synthesis, the `ai_search_post` tool utilizes the Yandex Yazeka AI model to process search results and return a concise, generated answer directly to the assistant. This allows an AI agent to not just find links, but to understand and summarize current events in a single step. For developers and power users, the server offers flexible integration options via the Model Context Protocol, supporting both remote SSE connections and local deployments using Docker or Python. It is designed for enterprise-grade stability, requiring a Yandex Search API key and a specific Folder ID with configured permissions to ensure secure access. By implementing this server, developers can transform a standard LLM into an autonomous agent capable of executing complex web-based tasks, while maintaining granular control over the search region and data retrieval through a standardized, extensible interface.

Category: AI & LLM Tooling

Tags: information-retrieval, real-time, web search, yandex

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How to install and configure Yandex Search MCP Server

1. Installation Remote Installation (Recommended) No local installation is required if using the remote SSE connection via npx. Local Installation If you prefer to run the server locally, clone the repository and install dependencies: bash git clone https://github.com/yandex/yandex-search-mcp-server.git cd yandex-search-mcp-server * For Python: pip install -r requirements.txt * For Docker: docker build -t yandex-mcp-server-image:latest . ---

2. Configuration To use this server, you must have a Yandex Search API Key (with yc.search-api.execute scope) and a Folder ID (with search-api.editor role).

Claude Desktop Add this to your claude_desktop_config.json: json { "mcpServers": { "yandexSearch": { "command": "npx", "args": [ "-y", "mcp-remote", "https://d5dj4o5pbnqgca1d546v.cmxivbes.apigw.yandexcloud.net:3000/sse", "--header", "ApiKey:<your_api_key>", "--header", "FolderId:<your_folder_id>" ] } } }

Cursor Add a Custom MCP in Settings with the following configuration: * Type: sse * URL: https://d5dj4o5pbnqgca1d546v.cmxivbes.apigw.yandexcloud.net:3000/sse json { "mcpServers": { "yandexSearch": { "type": "sse", "url": "https://d5dj4o5pbnqgca1d546v.cmxivbes.apigw.yandexcloud.net:3000/sse", "headers": { "ApiKey": "<your_api_key>", "FolderId": "<your_folder_id>" } } } }

Local Python Configuration (Standard MCP Client) json { "mcpServers": { "yandex-search-api": { "autoApprove": [], "disabled": false, "timeout": 60, "type": "stdio", "command": "env", "args": [ "SEARCH_API_KEY=<your_api_key>", "FOLDER_ID=<your_folder_id>", "python3", "/path/to/yandex-search-mcp-server/server.py" ] } } } ---

3. Available Tools * ai_search_post: Performs a real-time web search and returns an AI-generated answer based on results using the Yandex Yazeka AI model. * web_search_post: Performs a real-time web search and returns results with source citations. ---

4. Example Prompts Natural Language (Claude/Cursor): * "use yandexSearch to find information about the best AI Agent frameworks in 2025" * "Search for the latest news regarding Yandex Cloud updates using yandexSearch." Direct Tool Input (JSON): json { "query": "Who won the most recent Formula 1 race in 2025?", "search_region": "en" }

What you can do with Yandex Search MCP Server

Use Case 1: Coding with Post-Cutoff Frameworks and APIs Problem: Developers often work with libraries or frameworks that have been updated or released after the AI model's training data cutoff. Asking an LLM for help with a "2025 version" of an API usually results in hallucinations or outdated code snippets. Solution: By integrating the Yandex Search MCP into an IDE like Cursor or VS Code, the AI can perform a real-time search for the latest documentation. It can retrieve the exact syntax for new functions and apply them directly to the user's codebase. Example: A developer asks Cursor, "Update my project to use the latest Yandex Cloud SDK features released this month." Cursor uses web_search_post to find the new SDK documentation and suggests the correct updated code based on the search results.

Use Case 2: Instant Fact-Checking and Summarization Problem: Users often need to synthesize information from multiple news sources or verify a recent event, which usually requires opening several browser tabs and manually reading through them to find a consensus. Solution: The ai_search_post tool uses the Yandex Yazeka AI model to search the web and generate a single, coherent answer based on the most relevant results. This provides a "Perplexity-like" experience directly within Claude or other MCP-compatible clients. Example: A user asks Claude, "What were the key highlights of the international technology forum held yesterday?" Claude calls the ai_search_post tool, searches the latest news, and provides a summarized bullet-point list of events with the relevant context.

Use Case 3: Competitive Market Research with Cited Sources Problem: Market analysts need to identify new competitors or localized business trends, but they require the original sources to verify the data for formal reports. Solution: Using the web_search_post tool, an AI agent can perform deep searches across the web and return not just the information, but the specific source URLs. This allows the user to click through to the primary source for further validation. Example: An analyst tells their AI assistant, "Find the top 5 emerging fintech startups in the CIS region and provide links to their official websites and recent funding news." The MCP fetches the data and presents a table with the company name, a brief description, and the verified source link for each entry.

Use Case 4: Debugging Environment-Specific Errors Problem: When encountering a specific error message (e.g., a specific Linux kernel error or a niche…

Key facts

  • AI & LLM Tooling, Web Search & Research
  • information-retrieval, real-time, web search, yandex

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