Food Delivery
Facilitates AI agent interaction with a food delivery application's data and actions through the Model Context Protocol.
About
This tool functions as a local Model Context Protocol (MCP) server, designed to expose the capabilities of a food delivery application to AI agents. It intelligently maps application features—such as searching for restaurants, viewing menus, placing orders, and checking order status—to MCP's distinct primitives: Resources (read-only data like menus or cuisine lists) and Tools (action-oriented functions like ordering food or searching). By structuring these interactions, it enables an AI agent to understand context, execute actions, and provide a seamless, agent-driven experience for users interacting with a food delivery system. Its robust architecture separates database logic from server orchestration, ensuring maintainability and scalability.
Key Features
- 1 GitHub stars
- Enables AI agents to search for restaurants and browse menus
- Exposes food delivery capabilities via Model Context Protocol
- Allows AI agents to place and track food orders
- Distinguishes between MCP Resources for context and Tools for actions
- Utilizes Firestore for robust data management
Use Cases
- Building AI assistants for food ordering and delivery
- Integrating conversational AI with real-time food service data
- Automating food-related queries and transactions via agentic systems