ShipSmart is a sample Logistics AI Backend that demonstrates how to expose an existing FastAPI application as an MCP (Model Context Protocol) Server.

The project simulates a logistics company that manages customer orders, shipments, warehouses, and package tracking. A FastAPI backend exposes REST APIs, while an MCP server wraps those APIs so AI assistants (such as Claude Desktop, Cursor, or MCP Inspector) can interact with the logistics system using standardized MCP tools.

This project demonstrates how to build AI-ready applications without modifying existing business logic.

🏗️ Architecture

The MCP server does not access the database directly. Instead, it communicates with the FastAPI backend over HTTP, demonstrating how existing applications can be made AI-accessible without changing their internal architecture.

✨ Features

• FastAPI REST backend
• SQLite database using SQLAlchemy ORM
• MCP Server built using FastMCP
• AI-accessible logistics operations
• Sample logistics dataset
• Layered architecture (API → Services → Database)
• MCP Tools
• MCP Resources
• MCP Prompt

📁 Project Structure

logistics-mcp-server/
│
├── app/
│ ├── api/                      # FastAPI routes
│ ├── database/                 # Database connection, models and seed script
│ ├── mcp_server/
│ │ ├── api_client.py           # Calls FastAPI endpoints
│ │ ├── server_v1.py            # MCP Server using official MCP SDK
│ │ └── server_v2.py            # MCP Server using FastMCP package
│ ├── schemas/                  # Pydantic models
│ └── services/                 # Business logic
│
├── client/
│ └── streamlit_app.py          # Streamlit client application connecting to MCP Server
│
├── requirements.txt
├── .env
└── README.md

🛠 MCP Tools

The following tools are exposed through the MCP Server.

Tool	Description
get_order_details	Retrieve complete order information
search_orders	Search orders by customer, city or status
track_package	Retrieve shipment tracking details
cancel_order	Cancel an order
reschedule_delivery	Update the estimated delivery date
find_warehouse	Find warehouse serving a city

📄 MCP Resources

The project also exposes static resources.

Resource	Description
company://shipping-policy	Company shipping policy
company://supported-couriers	Supported courier partners
company://warehouse-locations	Warehouse locations

💬 MCP Prompt

Prompt	Description
summarize_tracking	Generates a professional customer-friendly shipment update from tracking information

🗄 Database

The project uses SQLite for simplicity.

Main entities:

• Customer
• Order
• OrderItem
• Shipment
• TrackingHistory
• Warehouse

🚀 Running the Project

1. Clone the repository

git clone <repository-url>
cd logistics-mcp-server

2. Create a virtual environment

Windows

python -m venv .venv
.venv\Scripts\activate

Linux / macOS

python3 -m venv .venv
source .venv/bin/activate

---

3. Install dependencies

pip install -r requirements.txt

4. Create the database

python -m app.database.create_db

5. Seed sample data

python -m app.database.seed

This populates the database with sample:

• Customers
• Orders
• Shipments
• Tracking history
• Warehouses

6. Start the FastAPI server

uvicorn app.api.main:app --reload

Swagger UI

http://localhost:8000/docs

7. Start the MCP Server

ShipSmart MCP Server contains two implementations:

MCP Server Implementations

File	Implementation	Import Used	Usage
server_v1.py	Official MCP SDK FastMCP	from mcp.server.fastmcp import FastMCP	Basic MCP server implementation
server_v2.py	FastMCP package	from fastmcp import FastMCP	Used with the Streamlit + Gemini client

The Streamlit application connects to server_v2.py.

Running servers

To start the MCP server:

python -m app.mcp_server.server_v1
#OR
python -m app.mcp_server.server_v2

Testing MCP Tools

You can test the MCP servers independently using the MCP Inspector:

mcp dev app/mcp_server/sever_v1.py
#OR
fastmcp dev inspector app/mcp_server/server_v2.py

The MCP Inspector allows you to test the available tools and verify that the server is exposing the expected MCP functionality.

8. Start the Streamlit Client

The Streamlit application acts as an MCP client and connects to server_v2.py.

Run:

streamlit run client/streamlit_app.py

💡 Example Questions for an AI Assistant

Once connected to the MCP Server, an AI assistant can answer questions like:

• Where is my order ORD-1001?
• Show me the tracking history for ORD-1002.
• Cancel order ORD-1003.
• Reschedule delivery for ORD-1004 to next Monday.
• Find the warehouse responsible for Pune.
• Search all delivered orders for Alice.

🧠 Why MCP?

Without MCP, every AI application would need custom integration code for each backend service.

MCP provides a standard interface that allows AI assistants to discover and invoke application capabilities through Tools, Resources, and Prompts.

This enables existing business applications to become AI-accessible with minimal changes.

🛠 Tech Stack

• Python
• FastAPI
• SQLAlchemy
• SQLite
• Pydantic
• HTTPX
• FastMCP (Model Context Protocol)
• Faker