Spring AI Resos MCP Server

Overview

The Spring AI Resos MCP server is a specialized gateway that bridges the gap between conversational AI assistants and restaurant‑booking backends. It exposes an OpenAPI‑derived interface that allows a Claude or other LLM to discover, query, and manipulate restaurant data in real time. By turning the ResOs API into an MCP‑compliant service, developers can add sophisticated booking capabilities to their AI workflows without writing custom HTTP clients or handling authentication manually.

Solving a common integration pain point

Restaurant reservation systems are traditionally accessed through web portals or native apps. Developers who want to offer voice or chat‑based booking need to expose a REST interface, handle OAuth or API keys, and map complex domain models. The Spring AI Resos MCP server eliminates this boilerplate by providing a ready‑made, fully documented MCP resource set. It automatically registers the available tools (search, reserve, cancel) and prompts that guide an LLM in constructing correct API calls. This means a developer can focus on business logic and user experience rather than plumbing.

What the server does

• Tool registration – The MCP server registers a set of tools that mirror the ResOs endpoints (search for restaurants, list tables, create reservations, etc.). Each tool is annotated with clear input schemas and output types so the AI can invoke them safely.
• Prompt orchestration – Built‑in prompts help the LLM understand how to phrase queries, interpret user intent, and format responses. These prompts are customizable through the configuration, allowing teams to tailor the conversation flow.
• Secure API delegation – The server accepts an optional ResOs API key and forwards authenticated requests to the external service. For restaurateurs, it can act as a thin wrapper over their own ResOs deployment, keeping credentials out of the LLM’s prompt space.
• Spring AI integration – The project ships a Spring Boot starter () that auto‑configures the MCP client, enabling seamless invocation from any Spring application. The starter also includes a lightweight ReactJS chatbot UI that demonstrates end‑to‑end interaction.

Key capabilities in plain language

• Search & filter – Users can ask for “restaurants near me with vegan options” and the server translates that into a structured API call.
• Real‑time availability – The MCP can query table slots for specific dates and times, returning up‑to‑date availability without caching issues.
• Reservation lifecycle – Create, confirm, or cancel bookings through a single conversational turn, with the server handling all necessary API calls.
• Extensible prompts – Add new conversational hooks or modify existing ones without touching the underlying code.
• Secure key management – Store API keys in environment variables or Spring profiles, keeping secrets out of source control.

Real‑world use cases

• Chatbot assistants – Embed the MCP in a customer support bot that handles bookings, cancellations, and wait‑list management.
• Voice commerce – Integrate with smart speakers so users can say “Book a table for two at 7 pm tomorrow” and receive confirmation instantly.
• Restaurant dashboards – Provide restaurateurs with a conversational interface to view upcoming reservations, manage table assignments, or adjust availability.
• Travel and hospitality apps – Offer travelers a single chat window to browse nearby dining options and secure reservations while planning itineraries.

Integration with AI workflows

Because the server follows MCP conventions, any LLM that understands tools and prompts can consume it directly. The AI first retrieves the tool list, then uses the supplied prompts to formulate a request, invokes the appropriate tool, and finally formats the response back to the user. Developers can embed this MCP into a larger Spring application, chain it with other AI services (e.g., sentiment analysis or recommendation engines), and orchestrate complex business logic—all while keeping the conversational layer clean and declarative.

Standout advantages

• Zero‑code API client – No need to write custom HTTP clients; the starter generates a type‑safe client automatically.
• OpenAPI first – The service is built from an OpenAPI derivative, ensuring that the contract stays in sync with ResOs updates.
• Spring Boot friendliness – Leverages Spring’s dependency injection, configuration, and profile system for easy deployment in microservice architectures.
• Demo UI – A ReactJS chatbot demonstrates the entire flow, lowering the learning curve for new adopters.

In summary, the Spring AI Resos MCP server turns a conventional restaurant‑booking API into an intelligent, conversational toolset. It abstracts away authentication, schema validation, and prompt engineering, allowing developers to focus on delivering engaging booking experiences powered by modern LLMs.