Building a Java AI agent with Google ADK, Vertex AI, and MCP tools
ANIL LALAM's tutorial walks through building a Java-based AI agent using Google ADK and Gemini on Vertex AI, integrating MCP server tools via JSON-RPC and SSE, and exposing the agent as a Spring Boot REST API.
Score breakdown
The article provides a concrete, end-to-end Java implementation showing how Google ADK's tool bridge pattern connects Gemini's reasoning to external systems via the standardized MCP protocol over JSON-RPC and SSE.
- 01The stack combines Google ADK, Gemini on Vertex AI, an MCP server, and Spring Boot, all implemented in Java.
- 02Vertex AI access requires enabling the Agent Platform API (`aiplatform.googleapis.com`) and creating a JSON service account key.
- 03Three environment variables route Gemini requests through Vertex AI: `GOOGLE_GENAI_USE_VERTEXAI`, `GOOGLE_CLOUD_PROJECT`, and `GOOGLE_CLOUD_LOCATION`.
ANIL LALAM's tutorial describes building an enterprise-grade agentic AI application that connects Google ADK, Gemini on Vertex AI, and an MCP server, all within a Java/Spring Boot stack. The agent uses Gemini as its reasoning and planning core, accessed through Vertex AI by enabling the Agent Platform API (`aiplatform.googleapis.com`) in a Google Cloud project and authenticating via a downloaded JSON service account key. Three environment variables — `GOOGLE_GENAI_USE_VERTEXAI`, `GOOGLE_CLOUD_PROJECT`, and `GOOGLE_CLOUD_LOCATION` — route all Gemini model requests through Vertex AI infrastructure, enabling enterprise authentication, security, governance, monitoring, and quota management.
These bridge methods contain no business logic themselves; instead, they forward requests to an MCP client, which constructs a JSON-RPC `tools/call` request and sends it to the MCP server over SSE.
On the tool integration side, the article explains that LLMs cannot directly call external systems, so Google ADK uses "tool bridges": Java methods annotated with `@schema` that expose a `name` and `description` to the LLM. These bridge methods contain no business logic themselves; instead, they forward requests to an MCP client, which constructs a JSON-RPC `tools/call` request and sends it to the MCP server over SSE. The article recommends keeping tool names and descriptions consistent between the ADK bridge and the MCP server definition to ensure alignment in the LLM's tool selection. Source code is available across two GitHub repositories linked in the article, and the tutorial explicitly builds on a companion article covering MCP server construction with Spring AI, JSON-RPC, and SSE.
Key facts
- 01The stack combines Google ADK, Gemini on Vertex AI, an MCP server, and Spring Boot, all implemented in Java.
- 02Vertex AI access requires enabling the Agent Platform API (`aiplatform.googleapis.com`) and creating a JSON service account key.
- 03Three environment variables route Gemini requests through Vertex AI: `GOOGLE_GENAI_USE_VERTEXAI`, `GOOGLE_CLOUD_PROJECT`, and `GOOGLE_CLOUD_LOCATION`.
- 04Tool bridges are Java methods annotated with `@schema`, exposing a `name` and `description` to the LLM without containing business logic.
- 05Tool bridge methods forward calls to an MCP client, which sends a JSON-RPC `tools/call` request to the MCP server over SSE.
- 06Source code is published across two GitHub repositories: `AgenticAIADKSpringBoot` and `MCPServerForTools`.
Topics
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