What is Gauntlet Incept Mcp

Gauntlet-Incept-MCP is a project designed to create the Incept program, which generates high-quality educational content tailored for K-8 students based on their knowledge levels and interests.

Use cases

Use cases include generating customized articles and question banks for various subjects, enhancing student engagement through tailored content, and supporting teachers with resources that align with students’ learning needs.

How to use

To use Gauntlet-Incept-MCP, developers can follow the guidelines provided in the documentation, particularly the MCP Server Guide, to set up the Model Context Protocol server and integrate it with the educational content generation system.

Key features

Key features include a structured project architecture with dedicated folders for API routes, data models, business logic, and utilities, as well as microservices for content storage and generation, and a comprehensive implementation checklist.

Where to use

Gauntlet-Incept-MCP is primarily used in the educational sector, particularly for K-8 educational institutions and content developers looking to create personalized learning materials.

Gauntlet-Incept

A system for generating high-quality educational content tailored to students’ knowledge levels and interests.

Project Description

This repository contains the code and resources for the Gauntlet-Incept project, which aims to build a system that generates high-quality educational content for K-8 students. The initial scope focuses on developing educational content in the form of articles and question banks for specific subject areas.

Documentation

• Project Overview - Detailed description of the project goals and requirements
• Implementation Checklist - Comprehensive checklist for project implementation
• Original Project Brief - Original project brief with detailed requirements
• MCP Server Guide - Guide for using the Model Context Protocol server with Claude Desktop

Project Structure

gauntlet-incept/
├── docs/                  # Documentation files
├── src/                   # Source code
│   ├── api/               # API routes
│   ├── models/            # Data models
│   ├── services/          # Business logic
│   ├── utils/             # Utility functions
│   ├── data/              # Data files
│   ├── tests/             # Test files
│   ├── index.js           # Entry point for REST API
│   └── mcp-server.js      # Model Context Protocol server
├── services/              # Microservices
│   ├── qti-service/       # QTI service for content storage
│   └── llm-service/       # LLM service for content generation
├── .env.example           # Example environment variables
├── .gitignore             # Git ignore file
├── package.json           # Node.js package file
├── docker-compose.yml     # Docker Compose configuration
├── Dockerfile             # Docker configuration
└── README.md              # This file

API Endpoints

The project implements six core API endpoints:

Question Endpoints

• POST /api/question/tag - Tag a question with subject, grade, standard, lesson, and difficulty
• POST /api/question/grade - Grade a tagged question against quality standards
• POST /api/question/generate - Generate a question based on tags or an example question

Article Endpoints

• POST /api/article/tag - Tag an article with subject, grade, standard, and lesson
• POST /api/article/grade - Grade a tagged article against quality standards
• POST /api/article/generate - Generate an article based on tags or an example article

Model Context Protocol (MCP) Server

In addition to the REST API, this project includes an MCP server that allows Claude Desktop to interact with the Gauntlet Incept system. This enables Claude to generate, tag, and grade educational content directly.

See the MCP Server Guide for details on how to set up and use the MCP server with Claude Desktop.

Getting Started

Prerequisites

• Git
• Node.js (v14 or higher)
• Access to the RDS PostgreSQL database (credentials provided by administrator)
• SSH key for database connection (if connecting through SSH tunnel)
• Docker and Docker Compose (optional, for containerized deployment)

Installation

1. Clone the repository

   git clone https://github.com/yourusername/Gauntlet-Incept.git
   

2. Navigate to the project directory

   cd Gauntlet-Incept
   

3. Install dependencies

   npm install
   

4. Copy the example environment file and update it with your values

   cp .env.example .env
   

5. Run the project

   npm start
   

Running with Docker

1. Build and start the containers

   docker-compose up -d
   

2. Access the API at http://localhost:3000
3. Access the MCP server at http://localhost:3001

Database Connection

This project connects to an Amazon RDS PostgreSQL instance with the following details:

• Host: alphacommoncrawl-core-reboot.cluster-caeuiwckzo1a.us-east-1.rds.amazonaws.com
• Port: 5432
• Database: core
• Username: postgres

Note: The password is stored in environment variables and not directly in the code for security reasons.

If you need to connect through an SSH tunnel, you’ll need to set up the tunnel separately before starting the application.

Development

Running in Development Mode

npm run dev

Running the MCP Server

npm run mcp

Running Tests

npm test

Linting

npm run lint

Project Checklist

• [x] Initialize Git repository
• [x] Create basic project structure
• [x] Add .gitignore file
• [x] Create initial commit
• [x] Set up project documentation
• [x] Create implementation checklist
• [x] Set up API routes and service structure
• [x] Implement placeholder functionality for core services
• [x] Set up Docker containerization
• [x] Implement MCP server for Claude Desktop integration
• [x] Configure connection to RDS PostgreSQL database
• [ ] Implement actual functionality with LLM integration
• [ ] Add tests
• [ ] Review and finalize

License

MIT

Contact

[Your contact information]