Spring MCP Server Demo

Overview

The Spring MCP Server Demo is a lightweight, Spring‑Boot powered implementation of the Model Context Protocol (MCP). It demonstrates how an AI assistant can be seamlessly extended with custom tools, resources, and prompts by exposing a well‑defined MCP interface. The server addresses the common pain point of integrating third‑party services into conversational AI workflows: developers often need to write bespoke adapters for each data source, which can be time‑consuming and error‑prone. By adhering to MCP, this demo provides a standardized contract that any Claude or other MCP‑compatible client can consume without modification.

At its core, the server offers a set of RESTful endpoints that expose resources (data entities), tools (executable actions), and prompts (pre‑formatted conversational snippets). These endpoints are automatically discovered by MCP clients, allowing an assistant to query the server for available capabilities and invoke them on demand. The demo’s Spring foundation means it can be easily extended with additional services such as database access, external APIs, or custom business logic—all while preserving the MCP contract. This modularity is particularly valuable for developers who want to add domain‑specific knowledge or automation without rewriting the assistant’s core logic.

Key features of the demo include:

• Tool Execution – Clients can call tool endpoints to perform operations such as data retrieval, calculations, or external API calls, with the server returning structured results that the assistant can incorporate into its responses.
• Resource Discovery – The server lists available resources (e.g., user profiles, inventory items) and their schemas, enabling the assistant to fetch or update data in a type‑safe manner.
• Prompt Templates – Predefined prompts can be requested by name, allowing the assistant to inject consistent phrasing or context into conversations.
• Sampling Configuration – Clients can adjust sampling parameters (temperature, top‑p) to fine‑tune the assistant’s output style directly from the server.

Real‑world use cases are abundant. A retail chatbot could leverage this server to query product inventories, calculate shipping estimates, or retrieve customer order histories. A finance assistant might use it to pull market data, execute trades via a broker API, or generate risk reports. In both scenarios, the MCP contract ensures that new capabilities can be added or updated without touching the assistant’s codebase.

Integration with AI workflows is straightforward: an MCP‑compatible client (e.g., Claude) automatically discovers the server’s endpoints, selects the appropriate tool or resource based on the user’s intent, and streams results back into the conversation. This plug‑and‑play model reduces development time, promotes reuse, and keeps AI assistants tightly coupled to the latest business logic. The Spring implementation also benefits from familiar tooling, dependency injection, and security features, giving teams confidence that the server can scale and be maintained in production environments.