MCP server for Context7

MCP server for Context7 is an application that provides AI assistants
with access to up-to-date library and framework documentation and code examples
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.
• Up-to-Date Documentation. Context7 provides AI assistants with access to fresh library and framework documentation, eliminating the problem of outdated model knowledge.
• Code Examples. In addition to documentation, the server returns relevant code examples for the requested libraries.
• AI Assistant Integration. Enables AI models to retrieve contextual library information during their workflow.
• 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, ensuring compatibility with a wide range of clients.
• HTTP Transport. The MCP server is accessible on port 8000 (HTTP), simplifying integration with various clients and tools.
• Minimal Resources. The application runs on a virtual machine with minimal configuration (2 vCPU, 2 GB RAM), reducing infrastructure costs.
• 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 for deploying the application and assign it the roles compute.editor, iam.serviceAccounts.admin, and vpc.admin on the deployment folder.
3. Assign the installer service account the lockbox.admin role separately on the Bearer token secret. This role is required to read and modify access permissions on the selected secrets; no folder-level Lockbox role is required. This also applies to secrets in another folder. During installation, the application creates a dedicated VM service account and grants it lockbox.payloadViewer only on the selected secrets.
4. In the management console select the Cloud Apps service.
5. In the left panel, select Application Store.
6. Select MCP Context7 and click the Use button.
7. 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.
   • External access to the MCP server (optional; disabled by default, enable for connections through the public IP address).
   • SSH access: select Disabled or Enabled. If SSH access is enabled, specify a username and an SSH public key.
8. Click the Install button and wait for the application to install.
9. After installation, two MCP server URLs are available under Application data:
   • MCP server URL (external): http://<public-ip>:8000/mcp — for connections over the internet when external access is enabled.
   • MCP server URL (internal): http://<private-ip>:8000/mcp — for clients with network access to the VM’s internal IP address. Yandex AI Studio uses the internal URL.
10. Configure your AI client 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 AI Studio and go to Agent Core → MCP servers.
2. Click Create MCP server and select the required MCP server under MCP servers from Yandex Cloud Marketplace.
3. Specify the server name and select an existing application instance, or create a new one by following the deployment instructions. When deploying a new server, the Save button becomes available only after the MCP server has been fully deployed and started.
4. After you select the application, the MCP server’s Network and Service account settings are filled in automatically.
5. Click Create. After the connection has been verified successfully, select the required tools and click Save. The MCP server will be registered in AI Studio and available for use by AI agents.

• Retrieving up-to-date documentation for libraries and frameworks (React, Next.js, LangChain, etc.) using an AI assistant.
• Searching for code examples for specific libraries via natural language.
• Eliminating the problem of outdated AI model knowledge when working with new library versions.
• Automatically obtaining contextual information about project dependencies.

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.