Spring AI integration with MCP (Model Context Protocol)

What is Spring AI integration with MCP (Model Context Protocol)?

Spring AI MCP extends the MCP Java SDK with Spring Boot integration, providing both client and server starters. It enables AI models to interact with external tools and resources in a standardized way through the Model Context Protocol (MCP).

How to use Spring AI integration with MCP (Model Context Protocol)?

Bootstrap your AI applications with MCP support using Spring Initializer. The example shows an MCP Server connecting to a PostgreSQL database, exposing tools annotated with @Tool, and an MCP Client that connects to the server. The LLM (e.g., Ollama) automatically selects the correct tool based on the prompt when tool names and descriptions are properly written.

Key features of Spring AI integration with MCP (Model Context Protocol)

• Provides both MCP Client and MCP Server Spring Boot starters
• Supports stdio and SSE transport mechanisms
• Exposes tools via @Tool and @ToolParam annotations
• Automates LLM tool selection based on prompt and tool metadata
• Integrates with Spring Data repositories for database queries

Use cases of Spring AI integration with MCP (Model Context Protocol)

• Let an AI assistant query a PostgreSQL database by matching prompts to predefined search tools
• Build a server that offers multiple data‑access tools (e.g., by name, by owner) for an LLM
• Create a client that connects to an MCP server and delegates tool invocation to an LLM
• Prototype an AI‑powered customer support agent that fetches account information from a database

FAQ from Spring AI integration with MCP (Model Context Protocol)

What is MCP?

MCP follows a client‑server architecture. The Host is the main application integrating an LLM; Clients maintain 1:1 connections with Servers; Servers expose tools, resources, and capabilities to clients.

What transport mechanisms does Spring AI MCP support?

It supports Standard Input/Output (stdio) for local process communication and Server‑Sent Events (SSE) for HTTP‑based communication.

What tools are exposed in the example?

The example exposes two tools: “Search Seller account by name” and “Find all Seller Accounts by owner”, both querying a PostgreSQL seller_account table.

How does the LLM choose which tool to call?

The LLM automatically selects the correct tool based on the prompt. Tool names and descriptions must be written clearly for proper matching.

What are the runtime requirements for the example?

Java (Spring Boot), the MCP server and client projects, a PostgreSQL database (run via Docker), and a local LLM (e.g., Ollama with any model) are required.