MCP Server
@dimahike
MCP Server について
A middleware server that acts as a bridge between Cursor IDE and AI models, validating AI responses using project context and Gemini.
基本情報
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概要
What is MCP Server?
MCP Server is a middleware server that acts as a bridge between Cursor IDE and AI models, validating AI responses using project context and the Gemini API. It is intended for developers using Cursor IDE who want to ensure AI-generated code or suggestions are consistent with their project’s context.
How to use MCP Server?
Clone the repository, install dependencies with npm install, create a .env file with your Gemini API key and configuration options, build the project with npm run build, and start the server with npm start. For development with hot-reloading use npm run dev. A local deployment script (./scripts/deploy-local.sh) is also provided for streamlined setup.
Key features of MCP Server
- Project context management
- AI response validation
- Integration with Gemini API
- Real‑time context updates
- Comprehensive logging system
- Easy local deployment
Use cases of MCP Server
- Validate AI responses against current project context
- Manage real‑time context updates inside Cursor IDE
- Bridge Cursor IDE with Gemini‑powered AI models
FAQ from MCP Server
What are the prerequisites for MCP Server?
Node.js v14+ recommended, npm or yarn, a Google Cloud account for Gemini API access, a Gemini API key, and Cursor IDE.
How do I obtain a Gemini API key?
The README does not detail how to obtain the key, but it states you need a Google Cloud account and a Gemini API key to use the server.
How do I deploy the server locally?
Use the provided script ./scripts/deploy-local.sh, which checks for a .env file, installs dependencies, builds the project, and starts the server in production mode. Alternatively follow the manual steps: npm install, npm run build, npm start.
What API endpoints does MCP Server expose?
GET /api/health for health checks, POST /api/context/initialize and /api/context/refresh for context management, and POST /api/validate and /api/cursor/validate for AI response validation.
Where are server logs stored?
Logs are saved in the logs/ directory, as shown in the project structure.
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