How are tools implemented in Spring Boot AI?

Tools are created as Spring Beans annotated with `@Tool` and `@ToolParam`, and registered as `MethodToolCallbackProvider`.

Does Spring Boot AI use a vector store?

Yes, it uses PGVector for RAG – the vector store is loaded with sample city data at startup.

What credentials does it need?

It reads `CONVERSATION_ID`, `GOOGLE_API_KEY`, `AWS_ACCESS_KEY_ID`, `AWS_SECRET_ACCESS_KEY` from the environment. Store these in your client's env block or a secrets manager — never commit them to a repository — and grant the narrowest scope that still covers your use case.

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Spring Boot AI MCP Server

🍀 🤖 Spring Boot AI - Evaluation Testing

Local serverstdio

What is the Spring Boot AI MCP Server?

🍀 🤖 Spring Boot AI - Evaluation Testing

What you get

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Maintainer setup documentation.

Recorded tools

Check the linked publisher documentation and your connected client's tool list. Names and availability can vary with the server version and account permissions.

Tool	What it does
spring-boot-starter	web or -webflux - to expose a REST API for the chat interface
spring-ai-starter-mcp-client	to use MCP
spring-ai-starter-vector-store	pgvector and spring-ai-advisors-vector-store - to enable RAG with PGVector
spring-ai-starter-model-ollama	to use Ollama models
spring-ai-starter-model-google	genai and spring-ai-starter-model-google-genai-embedding - to use Google Gemini models
spring-ai-starter-model	bedrock and spring-ai-starter-model-bedrock-converse - to use AWS Bedrock models

Configuration

Variable	Description	Required
CONVERSATION_ID	Configuration value read at startup.	Optional
GOOGLE_API_KEY	Credential the server authenticates with.	Yes
AWS_ACCESS_KEY_ID	Credential the server authenticates with.	Yes
AWS_SECRET_ACCESS_KEY	Credential the server authenticates with.	Yes