Smart Warehouse MCP Agent

# Claude-Powered MCP Agent for Smart Supply Chain This project simulates a smart warehouse system powered by Claude using Model Context Protocol (MCP) patterns. The system manages inventory, automated guided vehicles (AGVs), and order processing through a set of specialized agents coordinated by Claude. ## Project Structure ``` claude-mcp-agent-for-supply-chain/ ├── agents/ # MCP agent modules ├── simulation/ # Warehouse simulation logic ├── api/ # FastAPI endpoints ├── logs/ # Action and decision logs ├── claude_interface.py # Interface to Claude API ├── main.py # Main application entry point ``` ## Features - **MCP-style Modular Agents**: InventoryManager, AGVPlanner, RestockAgent, Coordinator - **Warehouse Simulation**: Inventory tracking, AGV movement, order processing - **Claude Integration**: Uses Anthropic's Claude API for decision-making - **API Endpoints**: FastAPI-based endpoints for interacting with the system ## Setup 1. Create a virtual environment: ``` python -m venv venv ``` 2. Activate the virtual environment: - Windows: `venv\Scripts\activate` - Unix/MacOS: `source venv/bin/activate` 3. Install dependencies: ``` pip install -r requirements.txt ``` 4. Set up environment variables: ``` cp claude.env.template claude.env ``` Then edit `claude.env` to add your Anthropic API key. 5. Run the application: ``` python main.py ``` ## API Endpoints - `GET /inventory`: Get current inventory status - `GET /agvs`: Get status of all AGVs - `POST /orders`: Create a new order - `POST /ask-agent`: Send a query to Claude agent - `GET /logs`: Get recent action logs ## Example Usage Example prompt to Claude: > The inventory for Product X is at 5 units, below the threshold of 10. Two AGVs are available. Suggest an optimal action. Claude will analyze the situation and return structured actions that the system can execute. ## License MIT