Model Context Protocol with Spring AI: Building MCP Clients and Servers in Java

说明与用途

模型上下文协议（MCP）使使用 Spring AI 的 Java 应用程序能够标准化与外部工具和资源的交互，减少代码重复并简化集成。

问题

AI applications need to integrate with many external tools and resources, but each integration often requires custom code, leading to maintenance challenges.

结果

By adopting MCP, developers can separate AI logic from external capability implementations, enabling reusable, maintainable integrations across multiple AI applications.

工作流

• 1. Define the external capability (e.g., getTemperature) in a Spring service.
• 2. Annotate the method with @McpTool and provide a description.
• 3. (Optional) Annotate parameters with @McpToolParam for clarity.
• 4. Build the application as an MCP server using Spring AI boot starters.
• 5. Deploy the MCP server so it exposes the tool via a standardized interface.
• 6. In the AI application, add the MCP client dependency.
• 7. Configure the MCP client to point to the MCP server URL.
• 8. The AI application can now discover available tools and invoke them during LLM reasoning.
• 9. The LLM calls the MCP tool, which forwards the request to the external API and returns the result.
• 10. The AI application uses the result to generate the final answer.

证据

MCP provides a standardized way for AI applications to interact with external tools and resources.

来源

Spring AI provides Boot starters and APIs for both sides of this architecture.

来源