spring-ai

This repository serves as a reference hub for a Spring AI course and related tooling rather than a standalone MCP server implementation. It contains resources, links, and tooling guidance for integrating Spring AI with large language models (LLMs) such as OpenAI within Spring Boot applications. Since there is no explicit MCP server entrypoint defined in the README, there is no configured MCP server to connect to from this document. Use the references herein to inform the design of your own MCP-enabled services: you can connect client applications to Spring AI-backed services or model runners by following the Spring AI documentation and the OpenAI platform docs, then apply typical MCP patterns (decoupled clients, protocol-compatible requests, and telemetry) in your own server implementations.

Prerequisites:

• Java 17+ and Maven or Gradle for Spring Boot projects
• A Java IDE (e.g., IntelliJ IDEA) or a text editor
• Optional: Docker and Docker Compose for local model runtimes and containerized services

Step-by-step:

1. Clone the repository: git clone https://github.com/your-org/spring-ai.git cd spring-ai

2. Verify Java and build tools are installed: java -version mvn -version # or gradle -version

3. Explore or create a Spring Boot project that uses Spring AI and connects to an LLM provider (e.g., OpenAI):

   • Add dependencies for Spring Boot and Spring AI as per the official documentation.
   • Configure the provider (API keys) in application.properties or application.yml.

4. Set up local model runtimes if needed (optional):

   • Install Docker and use Ollama or similar tools, following their setup docs.
   • If you plan to run containers, create a docker-compose.yml that starts a Spring Boot app and a local model endpoint.

5. Run the application: mvn spring-boot:run # or ./gradlew bootRun

6. Validate by hitting REST endpoints or WebFlux controllers that invoke LLM calls, and observe telemetry and metrics if configured.

Note: This repository provides reference links and concepts rather than a single runnable MCP server. Adapt the steps to your specific Spring AI integration and MCP-based client/server setup following the MCP protocol guidelines in your project.

Tips and considerations:

• Ensure API keys for providers like OpenAI are kept secure and not checked into version control; use environment variables or a secrets manager.
• If you deploy locally, you may want to configure Prometheus and Grafana for observability and to monitor Spring Boot metrics.
• When integrating MCP, design your client and server to be decoupled: use a clear request/response schema, version the protocol, and instrument traces (OpenTelemetry) to track LLM request flows.
• If you plan to use Docker Desktop for local runtimes, refer to the Docker Model Runner docs for guidance on running model containers alongside your Spring services.
• Refer to the Spring AI official docs and OpenAI platform docs for provider-specific configuration details and best practices.