DataLakeHouseMCPJava by Zabi82 - MCP Server

DataLakeHouseMCP Java: Model Context Protocol (MCP) Server

Overview

DataLakeHouseMCP Java is a Spring Boot-based MCP server that exposes resources and tools for interacting with data infrastructure components such as Kafka, Flink, and Trino/Iceberg. The server is designed for discoverability and integration with AI-powered MCP clients like Copilot Chat (VSCode/IntelliJ), Claude Desktop, and MCP Inspector.

Prerequisites

Lakehouse Setup: You must have a local lakehouse environment running with Kafka, Flink, Trino, and Iceberg. For a quickstart, follow the instructions in the flink-iceberg GitHub repository:
- Clone the repo and run the provided docker-compose setup.
- This will start Kafka (with Schema Registry), Flink, Trino, and Iceberg with sample data and tables.
- Example setup:
```
git clone https://github.com/zabi82/flink-iceberg.git
cd flink-iceberg
docker compose up -d
```
- Wait for all services to be healthy before starting the MCP server.
Java 21+ must be installed. Download from Adoptium.
Maven for dependency management and building the project.

Demo Outline (including Lakehouse setup from other repo)

File Structure

src/main/java/com/mcp/datalakehouse/McpServerApplication.java: MCP server entry point.
src/main/java/com/mcp/datalakehouse/tool/KafkaToolService.java: Kafka-related MCP tools.
src/main/java/com/mcp/datalakehouse/tool/FlinkToolService.java: Flink-related MCP tools.
src/main/java/com/mcp/datalakehouse/tool/TrinoToolService.java: Trino/Iceberg-related MCP tools.
src/main/resources/application.properties: Configuration properties for all tools.
pom.xml: Maven dependencies.

Features & Tool Capabilities

Kafka Tools

List Kafka Topics: Lists all Kafka topics available in the local cluster.
Peek Kafka Topic: Retrieves the latest N messages from a specified Kafka topic. Supports:
- Avro messages (using Schema Registry)
- JSON string messages
- Plain text messages
- Automatic deserializer selection and fallback
- Configurable number of messages (default: 10)

Flink Tools

Cluster Overview: Shows Flink cluster metrics: number of task managers, slots, jobs running/finished/cancelled/failed.
JobManager Metrics: Returns JobManager metrics (heap memory, CPU load, JVM/process stats).
TaskManagers Metrics: Returns TaskManagers metrics (heap memory, network IO, slot utilization).
List Flink Jobs: Lists all Flink jobs running on the cluster (IDs, names, status).
Flink Job Details: Returns details for one or more Flink jobs by job ID(s): status, vertices, configuration.
List TaskManagers: Lists all Flink TaskManagers and their details.
Probe JobManager/TaskManager Metrics: Query specific metrics by name.

Trino & Iceberg Tools

List Iceberg Tables: Lists all Iceberg tables in a specified Trino catalog.
List Trino Catalogs: Lists all catalogs available in the Trino cluster.
List Trino Schemas: Lists all schemas in one or more specified Trino catalogs (multi-catalog supported).
Get Iceberg Table Schema: Returns the schema (columns/types) of an Iceberg table.
Execute Trino Query: Executes a SQL query on Trino and returns results.
Iceberg Time Travel Query: Executes a time travel query on Iceberg tables using Trino. Supports ISO 8601 timestamp or snapshot ID.
List Iceberg Snapshots: Lists all snapshots for a given Iceberg table.

MCP Discovery & Inspector

All tools/resources are annotated for easy discovery by MCP clients.
Use MCP Inspector to visually inspect available tools, schemas, and try out tool invocations interactively.

Installation

Clone the repository

git clone <your-repo-url>
cd DataLakeHouseMCPJava

Build the project

mvn clean install

Running the MCP Server

This MCP server runs as a Spring Boot application. It can be launched via Maven or directly using the generated JAR file.

mvn spring-boot:run
# or
java -jar target/datalakehouse-mcp-java-0.1.0.jar

Configuring MCP Clients

Claude Desktop

Go to Settings > Integrations > Model Context Protocol (MCP).
Click "Add MCP Server" and set the executable path to your MCP server (e.g. java).
Set arguments to: -jar /path/to/DataLakeHouseMCPJava/target/datalakehouse-mcp-java-0.1.0.jar
Optionally, set the working directory to your project folder (e.g. /path/to/DataLakeHouseMCPJava).
Save and enable the integration.
Claude will launch the MCP server in stdio mode and auto-discover available MCP tools and resources.
Example claude_desktop_config.json:

{
  "mcpServers": {
    "mcp-data-lakehouse-java": {
      "command": "java",
      "args": [
        "-jar",
        "/path/to/DataLakeHouseMCPJava/target/datalakehouse-mcp-java-0.1.0.jar"
      ]
    }
  }
}

Copilot Chat in VSCode

Open Copilot Chat and go to MCP server configuration (usually in the extension settings or via command palette).
Add a new MCP server:
- Executable: java
- Arguments: -jar /path/to/DataLakeHouseMCPJava/target/datalakehouse-mcp-java-0.1.0.jar
- Working directory: /path/to/DataLakeHouseMCPJava
Save the configuration.
Example mcp.json:

{
  "servers": {
    "mcp-data-lakehouse-java-test": {
      "type": "stdio",
      "command": "java",
      "args": ["-jar", "/path/to/DataLakeHouseMCPJava/target/datalakehouse-mcp-java-0.1.0.jar"]
    }
  },
  "inputs": []
}

Copilot Chat in IntelliJ

Open Copilot Chat and go to MCP server configuration (usually in plugin settings).
Add a new MCP server:
- Executable: java
- Arguments: -jar /path/to/DataLakeHouseMCPJava/target/datalakehouse-mcp-java-0.1.0.jar
- Working directory: /path/to/DataLakeHouseMCPJava
Save the configuration.
Example mcp.json:

{
  "servers": {
    "mcp-data-lakehouse-java": {
      "type": "stdio",
      "command": "java",
      "args": [
        "-jar",
        "/path/to/DataLakeHouseMCPJava/target/datalakehouse-mcp-java-0.1.0.jar"
      ]
    }
  }
}

Example Prompts

You can use the following prompts in any MCP-enabled client with the provided Lakehouse setup

Show me all Kafka topics available in the cluster
Show some recent messages from page views
Provide me a high level overview of my Flink cluster
How many jobs are running and how many task slots are available in Flink ?
Provide details of the running jobs
What metrics are available for job manager and task manager
Get me JVM memory related metric values for both job manager and task manager
What are the different catalogs and schemas present in my iceberg environment
What are the tables present in ice-db schema 10 How many records are there in ice user page views currently
How much was the count 30 minutes ago Singapore time
How much was the count at 7:30 PM Singapore time
What are the most popular pages visited by users? Provide a pie chart with the page description to illustrate
What about the top regions in terms of user traffic to the site. Provide another pie chart to illustrate this

MCP Tool Discovery

All tools are annotated with descriptions. MCP clients and MCP Inspector will auto-discover available tools and their parameters, making it easy to interact programmatically or via chat.

Extending

Add new tools/resources by creating service classes in src/main/java/com/mcp/datalakehouse/tool/ and annotating with appropriate Spring AI MCP annotations. Follow the structure of existing tool services for best practices.

Testing & Troubleshooting

MCP Inspector Tool

You can use the MCP Inspector to test and troubleshoot the MCP server and its tools. This is especially useful for verifying tool interfaces, inspecting tool annotations, and simulating LLM interactions.

Usage

Run the following command in your project directory:

npx @modelcontextprotocol/inspector java -jar /path/to/DataLakeHouseMCPJava/target/datalakehouse-mcp-java-0.1.0.jar

This will start the MCP Inspector in stdio mode, allowing you to interactively test tool definitions and server responses. For more details, see the MCP Inspector documentation.

Ensure all dependencies are installed via Maven.
Check MCP client configuration for correct executable path and arguments.
Review logs for errors (e.g., missing modules, connection issues).
Use MCP Inspector for interactive testing and debugging of tool invocations.