Building AI Agents with the Kotlin Agent Development Kit (ADK)

A developer built a starter 'Hello World' AI agent using Kotlin and Google's native Kotlin Agent Development Kit (ADK). The agent uses Gemini to decide when to call a tool discovered from a local Kotlin Model Context Protocol (MCP) server, showcasing static typing benefits for agent configuration.

This tutorial builds a starter "Hello World" style agent using Kotlin and the native Kotlin version of the Agent Development Kit ADK . The full sample project is available on GitHub: This project is a runnable Kotlin Agent Development Kit ADK demo. A Kotlin LlmAgent uses Gemini to decide when to call a greet tool discovered from a local Kotlin Model Context Protocol MCP server. The project has two Gradle modules: agent : the Kotlin ADK agent, Gemini model configuration, MCP toolset, and interactive ReplRunner ; server : the Ktor MCP server that exposes greet . com.google.adk:google-adk-kotlin-core v0.6.0 io.modelcontextprotocol:kotlin-sdk-jvm v0.8.1 The Gradle wrapper is included. Create the local environment file: cp .env.example .env Set GOOGLE API KEY in .env , then load it: source ./set env.sh The file is ignored by Git. Start the Kotlin MCP server in one… Kotlin is a modern, statically typed programming language created by JetBrains. It runs on the Java Virtual Machine JVM , works alongside existing Java libraries, and is widely used for Android, backend, and multiplatform development. Static typing is especially useful when building agents. Agent configuration, tool schemas, and tool results can all be checked by the compiler before a prompt reaches the model. This sample uses Java 25 . If Java is not installed, SDKMAN https://sdkman.io/ is a convenient way to install and switch between JDK versions on Linux and macOS: After installing SDKMAN , list the available Java 25 distributions: sdk list java Install the Java 25 distribution you prefer, then verify the active version: java --version The project includes the Gradle wrapper, so you do not need to install Gradle separately. The Agent Development Kit ADK is Google's code-first framework for building and deploying AI agents. It provides the pieces needed to configure models, write agent instructions, connect tools, manage sessions, and run agents locally. Google provides the Kotlin quickstart and API documentation here: The complete Kotlin ADK source is also available on GitHub: Agent Development Kit ADK is designed for developers seeking fine-grained control and flexibility when building advanced AI agents that are tightly integrated with services in Google Cloud. It allows you to define agent behavior, orchestration, and tool use directly in code, enabling robust debugging, versioning, and deployment anywhere – from your laptop to the cloud. Rich Tool Ecosystem : Utilize pre-built tools, custom functions, OpenAPI specs, or integrate existing tools to give agents diverse capabilities, all for tight integration with the Google ecosystem. Code-First Development : Define agent logic, tools, and orchestration directly in Kotlin for ultimate flexibility, testability, and versioning. Modular Multi-Agent Systems : Design scalable applications by composing multiple specialized… The Kotlin SDK is published as com.google.adk:google-adk-kotlin-core . This tutorial uses Kotlin ADK 0.6.0 . You need a Gemini Developer API key to run the interactive agent. Create one in Google AI Studio: The MCP server and tool-discovery smoke test do not need an API key. Clone the sample repository and run the initialization script. It builds the project and creates a local .env file from the included template: git clone https://github.com/xbill9/adk-hello-world-kotlin cd adk-hello-world-kotlin source init.sh Output: Created .env from .env.example. Add your credentials before running the agent. Setup complete. Start ./server.sh, then run ./run.sh in another terminal. Edit .env and set your API key: GOOGLE API KEY=your-api-key Load it into the current shell: source set env.sh Note:Never commit .env . It is already listed in .gitignore . The sample has two Gradle modules: agent contains the Kotlin ADK agent and interactive command-line runner. server contains a Ktor MCP server that exposes the greet tool.The core agent is defined in GreetingAgent.kt . It configures Gemini, gives the agent its instruction, and connects an MCP toolset: return LlmAgent name = "kotlin greeting agent", description = "A Kotlin ADK agent that greets people through an MCP tool.", model = Gemini name = modelName, apiKey = apiKey, , instruction = Instruction """ You are a concise greeting assistant. When the user asks you to greet someone, always call the greet tool with that person's name. Return the greeting produced by the tool. """.trimIndent , , toolsets = listOf mcpToolset , LlmAgent brings together the model, instructions, and available tools. The model defaults to gemini-3.1-flash-lite , but you can select another model with the GEMINI MODEL environment variable. Unlike the TypeScript weather sample, this project keeps the tool in a separate process. The agent discovers and invokes it through the Model Context Protocol https://modelcontextprotocol.io/ . GreetingAgent.kt creates an McpToolset connected to the local server: val mcpToolset = McpToolset.McpToolsetConfig sseConnectionParams = McpConnectionParameters.Sse url = mcpServerUrl, sseEndpoint = "sse", , toolFilter = listOf "greet" , .toToolset The connection is lazy. When the agent needs its tools, ADK opens an MCP session, requests the tool list, and makes the greet schema available to Gemini. The tool filter limits this agent to that single tool. The server registers the tool in Tools.kt : server.addTool name = Config.Tools.GREET, description = "Get a greeting from a local HTTP server.", inputSchema = ToolSchema properties = buildJsonObject { put Config.Tools.GREET PARAM, buildJsonObject { put "type", "string" put "description", "The name to greet" }, }, required = listOf Config.Tools.GREET PARAM , , { request - // Read the name and return: Hello, <name } The agent and server communicate over HTTP using Server-Sent Events SSE . By default, the server listens at http://localhost:8080 , with /sse for the stream and /messages for client messages. A single command builds both modules, runs the unit tests, and checks Kotlin formatting: make check You can call the Gradle tasks directly: ./gradlew build ktlintCheck test The tests check that the ADK agent contains its MCP toolset and that the greeting logic returns the expected text. Because the greeting formatter is a plain Kotlin function, it can be tested without calling Gemini: @Test fun testFormatGreeting { val result = Tools.formatGreeting "Kotlin Developer" assertEquals "Hello, Kotlin Developer ", result } Run make format if ktlintCheck reports a style issue. The tool server and agent run as separate applications. Start the MCP server in one terminal: ./server.sh In a second terminal, load the environment and start the agent: source set env.sh ./run.sh The Gradle commands provide the same entry points: ./gradlew :server:run ./gradlew :agent:run Ask the agent to greet someone: Greet Kotlin Developer Gemini selects the discovered greet tool and supplies: {"param":"Kotlin Developer"} The MCP server returns: Hello, Kotlin Developer Type exit to close the agent. You can verify the MCP connection independently of the model. With the server running, use the Kotlin ADK smoke test: ./gradlew :agent:smokeMcp This connects through McpToolset and confirms that the agent can discover greet . It does not require GOOGLE API KEY . The repository also includes a direct Python JSON-RPC client: python3 test mcp.py It initializes an MCP session, lists the available tools, calls greet with Galaxy , and verifies the response Hello, Galaxy . This project deploys the Ktor MCP server as a container. The ADK agent remains a client and connects to the deployed service through MCP SERVER URL . Set your Google Cloud project, then run the deployment script: gcloud auth login gcloud config set project YOUR PROJECT ID ./cloudrun.sh The script submits cloudbuild.yaml , which builds the Docker image, pushes it to Container Registry, and deploys the service to Cloud Run. The sample stores active SSE sessions in memory, so the supplied Cloud Run configuration limits the service to one instance. It also allows unauthenticated access for demonstration purposes. Add authentication, authorization, stricter CORS rules, and shared session storage before using this design in production. After deployment, retrieve the service URL: gcloud run services describe adk-hello-world-kotlin \ --region us-central1 --format 'value status.url ' Point the local agent at that URL: export MCP SERVER URL="https://your-service-url" ./run.sh The Kotlin Agent Development Kit brings agent development to the JVM with familiar Kotlin and Gradle tooling: LlmAgent , Gemini, and instructions in Kotlin.