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MCP Server

MCP Server

Local
github.com/dimahikeDeveloper Tools0Updated 86w ago
Repository

About MCP Server

MCP Server is middleware for Cursor IDE that uses project context and Gemini to validate AI responses. It is for teams using Cursor who want context-aware response checks and local deployment.

Key features

  1. 01Project context management
  2. 02AI response validation
  3. 03Gemini API integration
  4. 04Real-time context updates
  5. 05Comprehensive logging

Use cases

  1. 01Initialize or refresh project context
  2. 02Validate AI responses against project context
  3. 03Check Cursor-specific responses
  4. 04Run the server locally

MCP support

The project describes itself as an MCP server and a bridge between Cursor IDE and AI models. It provides HTTP endpoints for context management and response validation; the README does not specify MCP tools or resources.

Configuration

Local server config is the same for every client. Copy it into your client’s MCP config file.

No install method listed yet

We haven’t collected a package or remote endpoint for this server. Follow the instructions in the project README.

Repository

Tools

No tool list yet

Local servers expose tools only once running on your machine, and the maintainer has not provided a tool list.

Details

From the project README

MCP Server

A middleware server that acts as a bridge between Cursor IDE and AI models, validating AI responses using project context and Gemini.

Features

  • Project context management
  • AI response validation
  • Integration with Gemini API
  • Real-time context updates
  • Comprehensive logging system
  • Easy local deployment

Prerequisites

  • Node.js (v14+ recommended)
  • npm or yarn
  • Google Cloud account (for Gemini API access)
  • Gemini API key
  • Cursor IDE

Getting Started

  1. Clone the repository

  2. Install dependencies:

    npm install

  3. Create a .env file in the root directory based on the provided .env.example:

    cp .env.example .env

    Then edit the file to add your Gemini API key and other configuration options.

  4. Build the project:

    npm run build

  5. Start the server:

    npm start

    For development with hot-reloading:

    npm run dev

Deployment

Local Deployment

Use the provided deployment script for a streamlined setup:

./scripts/deploy-local.sh

This script will:

  • Check for the presence of a .env file
  • Install dependencies
  • Build the project
  • Start the server in production mode

For more detailed deployment instructions, see USAGE.md.

API Endpoints

  • GET /api/health - Health check endpoint
  • POST /api/context/initialize - Initialize project context
  • POST /api/context/refresh - Refresh project context
  • POST /api/validate - Validate AI responses
  • POST /api/cursor/validate - Cursor IDE-specific validation endpoint

For detailed API documentation, see USAGE.md.

Project Structure

mcp-server/
├── src/
│   ├── api/
│   │   ├── controllers/
│   │   ├── middleware/
│   │   └── routes/
│   ├── config/
│   ├── context/
│   ├── models/
│   ├── services/
│   ├── types/
│   ├── utils/
│   └── index.ts
├── logs/           # Log files directory
├── scripts/        # Deployment and utility scripts
├── docs/           # Documentation
├── .env
├── .env.example
├── .gitignore
├── package.json
├── tsconfig.json
└── README.md

Documentation

License

ISC

Comments

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New to MCP?

From what the protocol is to installing your first server, these answers cover most first questions.

An open protocol that defines how AI applications connect to external tools and data in a uniform way. Clients talk to the model; MCP servers provide tools, resources and prompt templates. They exchange standard messages, so one server can be reused by many clients.