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Smart Warehouse MCP Agent

Smart Warehouse MCP Agent

Public
Ayancodes2003/claude-mcp-agent-for-supply-chain

Simulates a smart warehouse system using Model Context Protocol with modular agents for inventory management, AGV coordination, and order processing, powered by Claude AI and accessible via FastAPI endpoints.

python
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May 29, 2025
Updated Jun 4, 2025

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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

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