MCP server for Fetch

MCP server for Fetch is an application that provides AI assistants with the ability to fetch web page content from the internet and extract text as Markdown via the standard Model Context Protocol (MCP). The server is deployed on a virtual machine in Yandex Cloud.

Key Features and Benefits

• Yandex AI Studio Integration. The MCP server integrates with AI Studio MCP Servers — connect it to your AI agents in just a few clicks.
• Web Page Fetching. Enables AI assistants to download content from any URL on the internet.
• Markdown Text Extraction. Automatic conversion of HTML pages to clean Markdown for convenient processing by AI models.
• robots.txt Compliance. Respects robots.txt rules by default, with an option to disable.
• Customizable User-Agent. Ability to set a custom User-Agent for HTTP requests.
• Bearer Token Authorization. All requests to the MCP server are protected with a Bearer token stored in Yandex Lockbox.
• Standard MCP Protocol. Uses the open Model Context Protocol for AI model interaction.
• HTTP Transport. The MCP server is accessible on port 8000 (HTTP).
• Minimal Resources. The application runs on a virtual machine with minimal configuration (2 vCPU, 2 GB RAM).
• Docker Containerization. The server runs in Docker containers (MCP + nginx auth proxy).

1. Create a secret in Yandex Lockbox to store the Bearer token for authorization. The secret must contain a bearer_token key with an arbitrary token value.
2. Create a service account with the roles compute.editor, iam.serviceAccounts.admin, lockbox.payloadViewer, vpc.admin (the admin role includes all the specified roles).
3. In the management console select the Cloud Apps service.
4. In the left panel, select Application Store.
5. Select MCP Fetch and click the Use button.
6. Specify:
   • Application name.
   • (Optional) Application description.
   • The service account created earlier.
   • The cloud subnet where the virtual machine will be deployed.
   • Lockbox secret ID containing the Bearer token for authorization.
7. Click the Install button and wait for the application to install.
8. After installation, the MCP server will be available at http://<vm-ip>:8000.
9. Configure your AI client (Claude Desktop, Cursor, etc.) to connect to the MCP server, specifying the Authorization: Bearer <your-token> header.

Connecting the MCP server to Yandex AI Studio

1. In the management console open the AI Studio service and go to the MCP Hub section.
2. Click Create MCP server and choose the External MCP server type.
3. Specify the server name and the ID of the VPC network where the MCP server virtual machine is deployed. The network ID is available on the application page in the Application data section, VPC network ID field.
4. At the MCP server connection step, fill in the parameters with the values from the Application data section:
   • MCP server type — value from the MCP server type field.
   • MCP server URL — value from the MCP server URL (internal) field.
   • In the HTTP headers block, add an Authorization header with the value Bearer <bearer_token>, where <bearer_token> is the token from the Yandex Lockbox secret specified during application installation.
5. Click Connect. After a successful connection check, select the required tools and click Connect — the MCP server will be registered in AI Studio and available for use by AI agents.

• Fetching up-to-date information from the internet to answer user questions.
• Extracting documentation and article content for AI assistant analysis.
• Collecting data from web pages for research and reports.
• Checking availability and content of web resources.
• Obtaining Markdown text representation of web pages.

Yandex Cloud technical support is available 24/7. The types of requests you can submit and the appropriate response time depend on your pricing plan. You can switch to the paid support plan in the management console. You can learn more about the technical support terms here.