Order_Food_MCP
Enables users to browse a menu of 8 food items including pizzas, burgers, and drinks, and look up item details by ID through the get_menu tool.
README
Design principle: business logic (menu.py) is kept fully independent of MCP wiring (server.py), so it can be tested in milliseconds without a running server or LLM.
Menu Design
8 items, each with a unique id, name, and price (float, for now):
| ID | Name | Price |
|---|---|---|
| p1 | Margherita Pizza | 299 |
| p2 | Pepperoni Pizza | 349 |
| b1 | Veggie Burger | 99 |
| b2 | Chicken Burger | 129 |
| p3 | Farmhouse Pizza | 349 |
| dr1 | Cold Coffee | 69 |
| dr2 | Mango Shake | 79 |
Testing Strategy
Tests in test_menu.py cover:
- Menu size is within the 5–10 item range
- Every item has valid id/name/price
- All IDs are unique
- Lookup by valid ID returns the correct item
- Lookup by invalid ID returns
None(not an exception)
Run with:
uv run pytest test_menu.py -v
Status
- [done] Week 1: MCP fundamentals, hello-world server, local LLM verified
- [done] Week 2: Menu data model, mock data, lookup function, tests passing
- [done] Week 3: get_menu MCP tool, tests, live LLM demo (Claude Desktop)
- [ ] Week 4: Order logic (pricing, validation)
Week 3 Demo
Verified get_menu_tool end-to-end in Claude Desktop: asked "What food can I order?"
in natural language (no explicit tool mention), and the LLM correctly called
get_menu_tool and returned the full 8-item menu in chat.
Also verified via MCP Inspector: tool discoverable, returns valid JSON with ( cmd - npx @modelcontextprotocol/inspector uv run server.py ) id/name/price for all 8 items.
Recommended Servers
playwright-mcp
A Model Context Protocol server that enables LLMs to interact with web pages through structured accessibility snapshots without requiring vision models or screenshots.
Magic Component Platform (MCP)
An AI-powered tool that generates modern UI components from natural language descriptions, integrating with popular IDEs to streamline UI development workflow.
Audiense Insights MCP Server
Enables interaction with Audiense Insights accounts via the Model Context Protocol, facilitating the extraction and analysis of marketing insights and audience data including demographics, behavior, and influencer engagement.
VeyraX MCP
Single MCP tool to connect all your favorite tools: Gmail, Calendar and 40 more.
graphlit-mcp-server
The Model Context Protocol (MCP) Server enables integration between MCP clients and the Graphlit service. Ingest anything from Slack to Gmail to podcast feeds, in addition to web crawling, into a Graphlit project - and then retrieve relevant contents from the MCP client.
Kagi MCP Server
An MCP server that integrates Kagi search capabilities with Claude AI, enabling Claude to perform real-time web searches when answering questions that require up-to-date information.
E2B
Using MCP to run code via e2b.
Neon Database
MCP server for interacting with Neon Management API and databases
Exa Search
A Model Context Protocol (MCP) server lets AI assistants like Claude use the Exa AI Search API for web searches. This setup allows AI models to get real-time web information in a safe and controlled way.
Qdrant Server
This repository is an example of how to create a MCP server for Qdrant, a vector search engine.