Understanding Tool Calling in LLMs – Step-by-Step with REST and Spring AI

✅ What Spring AI Handles for You ✅ Tool schema generation ✅ Argument binding ✅ tool_call_id mapping mapping ✅ Message state management ✅ Parallel tool orchestration ✅ Sequential tool routing ✅ Spring Boot DI, validation, and observability ✅ Compatible with OpenAI, Mistral, Gemini, and others You keep writing business logic. Spring AI wires up everything else. 🔌 Bonus: Tool Calling via MCP — No Extra Code Need your tools to work beyond just chat — like inside other agents or frontend clients? Spring AI makes it effortless. Just add the MCP server starter: org.springframework.ai spring-ai-mcp-server-spring-boot-starter ✅ Every @Tool method you define becomes an MCP-compliant endpoint — with zero extra code. Just add a small config (like type: stdio or type: sse ) in your MCP server setup. No YAML. No codegen. No new annotations. This gives you instant interoperability with any client or platform that speaks the Model Context Protocol (MCP). 🔁 In most frameworks (including Python), MCP tools and LLM tools are defined separately. ✅ In Spring AI, your @Tool method is both — no duplication, no extra wiring. Most other frameworks treat LLM tools and interoperable tools like MCP separately — Spring AI bridges them natively.