How are tool parameters optimized for LLMs?

The skill provides patterns for detailed @ToolParam descriptions and examples, which help LLMs understand exactly what data to pass and in what format.

Does this skill support local LLM models?

Yes, it includes specific configurations for running local models like Llama 3.1 via Ollama alongside cloud-based models like Grok.

Is this skill suitable for production AI agents?

Absolutely. By enforcing use case validation and domain modeling, it prevents common issues where LLMs might trigger incorrect or unvalidated business actions.

What database technologies are compatible with this pattern?

The pattern is database-agnostic; while the example uses ClickHouse via JDBC, the Clean Architecture approach allows you to swap in any persistence layer.

What is the core benefit of using DDD with Spring AI MCP?

Using DDD ensures that AI tool definitions remain in the infrastructure layer while business logic is protected in the domain and application layers, making the system easier to test and maintain.

Spring AI MCP Architecture

Name: Spring AI MCP Architecture
Author: reynierre

byreynierre

Desarrollo de API

Implements Model Context Protocol tools using Spring AI and Domain-Driven Design for robust, clean retail analytics integrations.

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This skill provides a comprehensive blueprint for developing Model Context Protocol (MCP) tools within the Spring AI ecosystem. By leveraging Clean Architecture and Domain-Driven Design (DDD), it ensures a strict separation between tool definitions in the infrastructure layer and core business logic in the application layer. It is specifically designed for developers building conversational retail analytics systems, providing standardized patterns for tool annotations, parameter descriptions, and multi-model configurations (Grok/Ollama) to ensure LLMs interact reliably with enterprise data sources.

Características Principales

Clean Architecture implementation for MCP tool delegation
Standardized Spring AI tool and parameter annotations
Best practices for LLM-optimized tool descriptions
Domain-Driven Design patterns for AI tool development
Multi-model support for Grok and local Ollama deployments
0 GitHub stars

Casos de Uso

Standardizing MCP tool definitions across Spring Boot microservices
Implementing enterprise AI agents with decoupled business logic
Building conversational retail analytics interfaces for store performance

Acerca de

Características Principales

Clean Architecture implementation for MCP tool delegation
Standardized Spring AI tool and parameter annotations
Best practices for LLM-optimized tool descriptions
Domain-Driven Design patterns for AI tool development
Multi-model support for Grok and local Ollama deployments
0 GitHub stars

Casos de Uso

Standardizing MCP tool definitions across Spring Boot microservices
Implementing enterprise AI agents with decoupled business logic
Building conversational retail analytics interfaces for store performance