Spring Boot AI MCP Server

Spring Boot AI MCP server exists for a simple reason — assistants are far more useful when they can act on Spring Boot AI directly instead of describing what you should do. 🍀 🤖 Spring Boot AI - Evaluation Testing.

What you get

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What the assistant can call

Once Spring Boot AI is connected, these are the calls the assistant has available:

• 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
• QuestionAnswerAdvisor — fetches context from a vector store and augments the user input (RAG)
• MessageChatMemoryAdvisor — adds conversation history to the user input (chat memory)
• SimpleLoggerAdvisor — logs the chat history to the console (for debugging)
• Components — The Components tool exposed by this server

Configuration and credentials

You will need 4 environment variables: CONVERSATION_ID, GOOGLE_API_KEY, AWS_ACCESS_KEY_ID, AWS_SECRET_ACCESS_KEY. The server will not start without them, which is usually why the tools fail to appear on a first run. Keep credentials in your client's env block or a secrets manager rather than in a file you might commit.

Setting it up

Setup follows the usual MCP pattern — install or clone the server, register it in your client's configuration file, restart the client.

Choosing this one

Among the AI and media services options, the useful question is rarely "what can it do" but "what does it cost you to run" — permissions, credentials, and how much of your context its toolset consumes. Spring Boot AI's toolset — spring-boot-starter, spring-ai-starter-mcp-client, spring-ai-starter-vector-store and 7 more — is a fair guide to whether it matches your workflow. It is maintained by rogervinas; worth a glance at recent repository activity before you build anything load-bearing on it.

SyncDev reviews every entry in this directory against the project's own documentation before publishing, and revisits them as servers change.

Before you rely on it

• It runs with your machine's permissions. That is convenient and also the reason to think about what you point it at before you approve a tool call.
• With 10 tools registered it takes up a noticeable share of the context window; turn it off in projects that never touch Spring Boot AI.
• Missing credentials fail quietly in some clients — if no tools show up, check the environment block first.
• MCP clients confirm each tool call by default. Leave that on until you have watched what the spring boot ai mcp server does with a few real requests.