Uber Eats MCP Server

The Uber Eats MCP Server is a proof‑of‑concept that demonstrates how the Model Context Protocol can be leveraged to create a fully functional AI‑powered interface for a real‑world food‑delivery platform. By exposing Uber Eats’ data and actions through MCP, developers can build assistants that query menus, place orders, or retrieve delivery status—all without writing custom API wrappers. The server translates standard MCP messages into calls against Uber Eats’ internal services, then streams the results back to an LLM client in a format that is immediately consumable by Claude or other MCP‑compatible models.

At its core, the server solves the problem of fragmented data access. Food‑delivery platforms expose a multitude of endpoints—restaurant listings, menu items, inventory checks, order placement, and tracking—all with different authentication schemes and response structures. The MCP server acts as a unified façade that normalises these interactions into a single, declarative protocol. Developers no longer need to manage OAuth flows or handle disparate JSON schemas; instead they send a simple request and receive structured data that the LLM can reason about. This abstraction dramatically reduces integration time and improves reliability, especially in rapid‑iteration environments where new features must be tested against live data.

Key capabilities of the Uber Eats MCP Server include:

• Resource discovery – Exposes a catalog of available endpoints (e.g., , ) that an LLM can introspect to understand what operations are possible.
• Tool execution – Allows the assistant to perform actions such as placing an order or updating delivery status, with built‑in safety checks that prevent accidental modifications during testing.
• Prompt templating – Provides pre‑defined prompts that guide the LLM in formatting requests, ensuring consistent query syntax and error handling.
• Sampling control – Enables fine‑grained tuning of LLM output (temperature, top‑p) directly from the MCP interface, allowing developers to balance creativity and determinism.

Real‑world scenarios that benefit from this server include:

• Customer support bots that can pull a user’s current order status or suggest nearby restaurants based on location data, all powered by the same LLM that handles natural language queries.
• Voice‑enabled ordering where a smart speaker asks the assistant to “order pizza from Domino’s” and the MCP server translates that into a signed API call.
• Analytics dashboards that let analysts ask questions like “Which menu item sold the most last week?” and receive structured tables without manual SQL queries.

Integration into AI workflows is straightforward. Once the MCP server is running, any LLM client that supports the protocol can issue or requests over stdio, HTTP, or WebSocket. The server handles authentication transparently, logs activity for auditability, and can be scaled behind a reverse proxy if needed. Developers can also extend the server with custom tools—such as price‑comparison or recommendation engines—without touching the core MCP logic.

What sets this Uber Eats MCP Server apart is its end‑to‑end proof of concept that bridges a complex, commercial API ecosystem with the simplicity of MCP. It showcases how LLMs can be empowered to perform real‑world transactions while maintaining a clean separation of concerns, making it an invaluable template for any team looking to integrate food‑delivery services into conversational AI experiences.