What is Vibe Model Context Protocol Server

Vibe-Model-Context-Protocol-Server (VMCPS) is a Python-based server designed for managing and analyzing code context to assist AI-driven development, eliminating the need for costly subscription APIs.

Use cases

Use cases include analyzing codebases for AI integration, managing project dependencies, generating context for files, and providing real-time feedback during development.

How to use

To use VMCPS, clone the repository, set up a virtual environment, install dependencies, and configure environment variables. Start the server and use the provided API endpoints to analyze projects and retrieve context.

Key features

Key features include real-time code analysis, automatic file change detection, secure authentication, RESTful API for context management, project structure analysis, dependency tracking, and cross-platform support for Windows, Linux, and macOS.

Where to use

VMCPS is suitable for software development environments, particularly in projects that require AI assistance for code analysis and context management.

Vibe Model Context Protocol Server (VMCPS)

A Python-based server for managing and analyzing code context for AI-assisted development.

Features

• Real-time code analysis and context generation
• Automatic file change detection
• Secure authentication and authorization
• RESTful API for context management
• Project structure analysis
• Dependency tracking
• Cross-platform support (Windows, Linux, macOS)

API Endpoints

• POST /token - Get authentication token
• POST /analyze - Analyze a project
• GET /context - Get context for a file or project
• GET /dependencies - Get project dependencies
• GET /structure - Get project structure

Security

• JWT-based authentication
• Password hashing with bcrypt
• CORS protection
• Environment-based configuration

Installation

1. Clone the repository:

git clone https://github.com/non-npc/Vibe-Model-Context-Protocol-Server.git
cd mcps

2. Create a virtual environment:

python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate

3. Install dependencies:

pip install -r requirements.txt

4. Configure environment variables:

• Copy .env.example to .env
• Update the values in .env as needed

Example API Usage (curl commands)

Start the server:

python -m mcps.main

1. Get token:

curl -X POST "http://localhost:8000/token" -H "Content-Type: application/x-www-form-urlencoded" -d "username=admin&password=admin"

2. Analyze project (replace with actual token):

curl -X POST "http://localhost:8000/analyze" -H "Authorization: Bearer <token>" -H "Content-Type: application/json" -d '{"project_path": "/path/to/your/project"}'

3. Get context:

curl -X GET "http://localhost:8000/context" -H "Authorization: Bearer <token>"

Access the API:

• The server will be running at http://localhost:8000
• API documentation is available at http://localhost:8000/docs

Development

1. Install development dependencies:

pip install -r requirements-dev.txt

2. Run tests:

pytest

Contributing

1. Fork the repository
2. Create a feature branch
3. Commit your changes
4. Push to the branch
5. Create a Pull Request

License

MIT License