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

The Model Context Protocol (MCP) is an open standard, originally proposed by Anthropic, that defines how AI models communicate with external tools and data sources in a structured way. Spring AI 1.x introduced first-class support for MCP, making it straightforward to build both MCP clients (Spring apps that call MCP-compatible tool servers) and MCP servers (Spring apps that expose their own tools to MCP-aware models).

In the client role, Spring AI can connect to any MCP-compatible tool server — a local process or a remote HTTP/SSE endpoint — and automatically register its exposed tools as Spring AI functions that the LLM can invoke during a conversation.

// Declare an MCP client connecting to a local filesystem MCP server
@Bean
public McpSyncClient filesystemMcpClient() {
    return McpClient.sync(
        new StdioClientTransport(
            new ServerParameters("npx",
                List.of("-y", "@modelcontextprotocol/server-filesystem",
                        "/tmp/data"))),
        McpSchema.Implementation.builder()
            .name("filesystem-client").version("1.0").build()
    ).build();
}

In the server role, a Spring Boot application annotated with @Tool methods can expose those methods as MCP-compliant tools consumable by Claude Desktop, VS Code Copilot, or any other MCP-aware client. This is particularly powerful for building enterprise AI assistants that need controlled access to internal data sources.

Spring AI's MCP support is layered on top of the existing tool-calling abstraction — MCP tools appear to the rest of the application exactly like locally registered function beans.

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