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

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

This service uses Model Context Protocol (MCP) server code to provide information about office supplies inventory from a CSV file. It allows AI assistants to query and retrieve inventory data using the MCP standard, with no local server installation needed when used with the cloud-hosted version and web-based NANDA host client.

How to use Office Supplies Inventory NANDA Service using MCP Server + NANDA Registry + NANDA host client?

Set up locally by cloning the repository, creating a Python virtual environment (using venv or Conda), installing dependencies from requirements.txt, and running python officesupply.py. Test with MCP Inspector using SSE transport at http://localhost:8080/sse. For cloud deployment, deploy to AWS AppRunner using the provided build.sh and run.sh scripts. Register the server on the NANDA Registry, then use it in the NANDA host client at nanda.mit.edu with an Anthropic API key.

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

• Provides two MCP tools: get_items and get_item_info
• Reads inventory data from a CSV file
• Designed for deployment on AWS AppRunner
• Registers on the NANDA Registry for discovery
• Works with the web-based NANDA host client
• Adaptable to any standard inventory by editing the CSV file

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

• Allow AI assistants to list all available office supplies
• Retrieve detailed information about a specific item by name
• Build consumer-facing web apps for any CSV-based inventory
• Enable cloud-hosted inventory queries without local setup

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

What are the prerequisites for running this server?

Python 3.9 or higher and the dependencies listed in requirements.txt are required.

How is the inventory data stored and managed?

Data is stored in a CSV file (inventory.csv) that must include at least an item_name column. Additional columns are returned as part of item details.

How do I test the server locally?

Run python officesupply.py, then use the MCP Inspector with SSE transport connecting to http://localhost:8080/sse to test the get_items and get_item_info tools.

How do I deploy this server to the cloud?

Deploy to AWS AppRunner by providing the source code repository, using Python 3.11 runtime, build command ./build.sh, run command ./run.sh, and port 8080.

How do I use the server with an AI assistant?

Register the server on the NANDA Registry, then add it in the NANDA host client at nanda.mit.edu with your Anthropic API key to query inventory.