Office Supplies Inventory NANDA Service using MCP Server + NANDA Registry + NANDA host client

Created By

izzyfondu8 months ago

Content

Office Supplies Inventory NANDA Service using MCP Server + NANDA Registry + NANDA host client

Create a NANDA service using Model Context Protocol (MCP) server code that provides information about office supplies inventory. This service allows AI assistants to query and retrieve information about office supplies using the MCP standard. You will use cloud hosted server and a web based NANDA host client. No need to install a local server.

You can deploy a consumer facing web-app for any standard inventory using the same framework.

Overview

This project implements a NANDA service using MCP server code that serves office inventory data from a CSV file. It provides tools that allow AI assistants to:

Get a list of all available items in the inventory
Retrieve detailed information about specific items by name

Prerequisites

Python 3.9 or higher
Dependencies listed in requirements.txt

Files in this Repository

officesupply.py: The main server implementation
inventory.csv: CSV file containing the office supply inventory data
build.sh: Script for setting up the environment
run.sh: Script for running the server
requirements.txt: List of Python dependencies

Quick Start

Local Setup

Clone this repository:

git clone https://github.com/aidecentralized/nanda-servers.git
cd office-supplies-shop-server

Choose one of the environment setup options below:

Option A: Using Python venv

Create a Python virtual environment:
```
python -m venv venv
```
Activate the virtual environment:
- On Linux/macOS:
```
source venv/bin/activate
```
- On Windows:
```
venv\Scripts\activate
```
Install dependencies:
```
pip install -r requirements.txt
```

Option B: Using Conda

Create a new conda environment:

conda create --name inventory_env python=3.11

Activate the conda environment:
```
conda activate inventory_env
```
Install dependencies:
```
pip install -r requirements.txt
```

Running the Server Locally to Test

After setting up your environment using either option above:

Run the server:
```
python officesupply.py
```
The server will be available at: http://localhost:8080

Testing with MCP Inspector

Install the MCP Inspector:
```
npx @modelcontextprotocol/inspector
```
Open the URL provided by the inspector in your browser
Connect using SSE transport type
Enter your server URL with /sse at the end (e.g., http://localhost:8080/sse)
Test the available tools:
- get_items: Lists all item names in the inventory
- get_item_info: Retrieves details about a specific item

CSV Data Format

The server expects an inventory.csv file with at least the following column:

item_name: The name of the inventory item

Additional columns will be included in the item details returned by get_item_info.

Within this purview, you can edit the CSV file for your requirements, and the MCP server should work for your CSV file as well.

Deployment

Preparing for Cloud Deployment

Make sure your repository includes:
- All code files
- requirements.txt
- build.sh and run.sh scripts
Set executable permissions on the shell scripts:
```
chmod +x build.sh run.sh
```
For Windows, run
```
wsl chmod +x build.sh run.sh
```

Create AWS account

Deploying to AWS AppRunner

Create AWS account
Add your credit card for billing
Go to AWS AppRunner (https://console.aws.amazon.com/apprunner)
Log in (if you’re not already)
Once you're in the App Runner dashboard, you’ll see a blue “Create service” button near the top right of the page. Click that.
Create a new service from your source code repository
Configure the service:
- Python 3.11 runtime
- Build command: ./build.sh
- Run command: ./run.sh
- Port: 8080
Deploy and wait for completion
Test the public endpoint with MCP Inspector

Registering on NANDA Registry

Go to NANDA Registry
Login or create an account
Click "Register a new server"
Fill in the details:
- Server name
- Description
- Public endpoint URL (without /sse)
- Tags and categories
Register your server