This MCP server implements a production-ready, multi-agent, multi-tool backend builder system. Four specialized agents collaborate to design, implement, validate, and deploy backend applications: Architect (planning system design and API specs), Coder (implementing code and models), Ops (handling deployment, testing, and operations), and Reviewer (code quality and production-readiness validation). Tools are chained and orchestrated to allow agents to leverage a filesystem workspace, execute shell commands, analyze code quality, and integrate with external MCP servers. The system can generate complete RESTful APIs, database schemas, authentication, WebSocket services, background tasks, documentation, tests, and Docker/CI/CD configurations, all while enforcing a production-ready checklist throughout the workflow. To interact with the system, you connect to the configured MCP servers (filesystem and terminal in this setup) and use the orchestrator (main.py) to kick off architecture planning, coding, and validation cycles. The workflow supports incremental development with user feedback loops, leveraging Gemini for reasoning and Groq for rapid code generation, along with security and quality checks.

Typical usage involves supplying a project name and description, then running the orchestrator to generate a complete backend solution with proper structure, tests, documentation, and deployment configurations. You can customize tool access and supply API keys for Gemini and Groq to enable advanced reasoning and fast code generation within the project workspace.

Prerequisites:

• Python 3.8+
• Virtual environment recommended
• Gemini API key
• Groq API key (optional but recommended)

Installation steps:

1. Clone or navigate to the project directory: mkdir -p <your-project-dir> && cd <your-project-dir>

2. Install Python dependencies: python -m venv venv source venv/bin/activate # on Windows use: venv\Scripts\activate pip install -r requirements.txt

3. Install MCP Terminal server (required for MCP integration): npm install -g @dillip285/mcp-terminal

4. Configure environment keys: cp .env .env.local

   Edit .env.local with your actual API keys

   Example:

   GEMINI_API_KEY=your_gemini_api_key_here

   GROQ_API_KEY=your_groq_api_key_here

5. Run the orchestrator (example): python main.py --project "AI Backend" --description "Production-ready backend with multi-agent and multi-tool collaboration"

Notes and tips:

• Ensure the filesystem MCP server URL is reachable and the workspace path is writable by your environment.
• The Terminal MCP server uses npx to run the terminal in the local environment; ensure Node.js is installed if you plan to use local terminal commands.
• Keep Gemini and Groq API keys secure and monitor usage costs, as advanced reasoning and code generation can consume API quotas.
• The system enforces a production checklist; if a step fails, review the corresponding agent outputs and re-run the relevant phase.
• If you need to customize tool access, modify the config files under config/ to add or remove tools (e.g., code analysis, tests, deployment scripts).
• For local development, ensure your Docker and CI/CD configurations align with your deployment target (cloud provider, Kubernetes, etc.).