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iunera/druid-mcp-server: A comprehensive Model Context Protocol (MCP) server for Apache Druid that provides extensive tools, resources, and AI-assisted prompts for managing and analyzing Druid clusters. Built with Spring Boot and Spring AI, this server enables seamless integration between AI assistants and Apache Druid through standardized MCP protocol.

A comprehensive Model Context Protocol (MCP) server for Apache Druid that provides extensive tools, resources, and prompts for managing and analyzing Druid clusters.

Developed by iunera - Advanced AI and Data Analytics Solutions

This MCP server implements a feature-based architecture where each package represents a distinct functional area of Druid management. The server provides three main types of MCP components:

Tools - Executable functions for performing operations
Resources - Data providers for accessing information
Prompts - AI-assisted guidance templates

Learn how to integrate AI agents with Apache Druid using the MCP server. This tutorial demonstrates time series data exploration, statistical analysis, and data ingestion using natural language with AI assistants like Claude, ChatGPT, and Gemini.

Click the thumbnail above to watch the video on YouTube

Spring AI MCP Server integration
Tool-based architecture for MCP protocol compliance
STDIO and SSE transport support
Comprehensive error handling
Customizable prompt templates
Feature-based package organization

When connected to an MCP client, you can inspect the available tools, resources, and prompts through the MCP inspector interface:

The tools interface shows all available Druid management functions organized by feature areas including data management, ingestion management, and monitoring & health.

The resources interface displays all accessible Druid data sources and metadata that can be retrieved through the MCP protocol.

The prompts interface shows all AI-assisted guidance templates available for various Druid management tasks and data analysis workflows.

Java 24
Maven 3.6+
Apache Druid cluster running with router on port 8888

# Build the application
mvn clean package -DskipTests

# Run the application
java -jar target/druid-mcp-server-1.0.0.jar

The server will start on port 8080 by default.

For detailed build instructions, testing, Docker setup, and development guidelines, see development.md.

Installation from Maven Central

If you prefer to use the pre-built JAR without building from source, you can download and run it directly from Maven Central.

Java 24 JRE only

# Create a directory for the application
mkdir druid-mcp-server && cd druid-mcp-server

# Download the JAR from Maven Central
curl -L -o druid-mcp-server-1.0.0.jar \
  "https://repo.maven.apache.org/maven2/com/iunera/druid-mcp-server/1.0.0/druid-mcp-server-1.0.0.jar"

# Run with SSE Transport (HTTP-based, default)
java -jar druid-mcp-server-1.0.0.jar

# OR run with STDIO Transport (recommended for LLM clients)
java -Dspring.ai.mcp.server.stdio=true \
     -Dspring.main.web-application-type=none \
     -Dlogging.pattern.console= \
     -jar druid-mcp-server-1.0.0.jar

If you prefer to use Docker, you can run the pre-built Docker image directly from Docker Hub without any local Java installation.

Docker installed and running

# Pull the latest Docker image
docker pull iunera/druid-mcp-server:latest

# Run with SSE Transport (HTTP-based, default)
docker run -p 8080:8080 \
  -e DRUID_BROKER_URL=http://your-druid-broker:8082 \
  -e DRUID_COORDINATOR_URL=http://your-druid-coordinator:8081 \
  iunera/druid-mcp-server:latest

# OR run with STDIO Transport (recommended for LLM clients)
docker run --rm -i \
  -e SPRING_AI_MCP_SERVER_STDIO=true \
  -e SPRING_MAIN_WEB_APPLICATION_TYPE=none \
  -e LOGGING_PATTERN_CONSOLE= \
  -e DRUID_BROKER_URL=http://your-druid-broker:8082 \
  -e DRUID_COORDINATOR_URL=http://your-druid-coordinator:8081 \
  iunera/druid-mcp-server:latest

Replace your-druid-broker and your-druid-coordinator with your actual Druid cluster endpoints.

For detailed development information including build instructions, testing guidelines, architecture details, and contributing guidelines, see development.md.

Available Tools by Feature Feature Tool Description Parameters Datasource listDatasources List all available Druid datasource names None Datasource showDatasourceDetails Show detailed information for a specific datasource including column information datasourceName (String) Datasource killDatasource Kill a datasource permanently, removing all data and metadata datasourceName (String), interval (String) Lookup listLookups List all available Druid lookups from the coordinator None Lookup getLookupConfig Get configuration for a specific lookup tier (String), lookupName (String) Lookup updateLookupConfig Update configuration for a specific lookup tier (String), lookupName (String), config (String) Segments listAllSegments List all segments across all datasources None Segments getSegmentMetadata Get metadata for specific segments datasourceName (String), segmentId (String) Segments getSegmentsForDatasource Get all segments for a specific datasource datasourceName (String) Query queryDruidSql Execute a SQL query against Druid datasources sqlQuery (String) Retention viewRetentionRules View retention rules for all datasources or a specific one datasourceName (String, optional) Retention updateRetentionRules Update retention rules for a datasource datasourceName (String), rules (String) Compaction viewAllCompactionConfigs View compaction configurations for all datasources None Compaction viewCompactionConfigForDatasource View compaction configuration for a specific datasource datasourceName (String) Compaction editCompactionConfigForDatasource Edit compaction configuration for a datasource datasourceName (String), config (String) Compaction deleteCompactionConfigForDatasource Delete compaction configuration for a datasource datasourceName (String) Compaction viewCompactionStatus View compaction status for all datasources None Compaction viewCompactionStatusForDatasource View compaction status for a specific datasource datasourceName (String) Feature Tool Description Parameters Ingestion Spec createBatchIngestionTemplate Create a batch ingestion template datasourceName (String), inputSource (String), timestampColumn (String) Ingestion Spec createIngestionSpec Create and submit an ingestion specification specJson (String) Supervisors listSupervisors List all streaming ingestion supervisors None Supervisors getSupervisorStatus Get status of a specific supervisor supervisorId (String) Supervisors suspendSupervisor Suspend a streaming supervisor supervisorId (String) Supervisors startSupervisor Start or resume a streaming supervisor supervisorId (String) Supervisors terminateSupervisor Terminate a streaming supervisor supervisorId (String) Tasks listTasks List all ingestion tasks None Tasks getTaskStatus Get status of a specific task taskId (String) Tasks shutdownTask Shutdown a running task taskId (String) Feature Tool Description Parameters Basic Health checkClusterHealth Check overall cluster health status None Basic Health getServiceStatus Get status of specific Druid services serviceType (String) Basic Health getClusterConfiguration Get cluster configuration information None Diagnostics runDruidDoctor Run comprehensive cluster diagnostics None Diagnostics analyzePerformanceIssues Analyze cluster performance issues None Diagnostics generateHealthReport Generate detailed health report None Functionality testQueryFunctionality Test query functionality across services None Functionality testIngestionFunctionality Test ingestion functionality None Functionality validateClusterConnectivity Validate connectivity between cluster components None Available Resources by Feature Feature Resource URI Pattern Description Parameters Datasource druid://datasource/{datasourceName} Access datasource information and metadata datasourceName (String) Datasource druid://datasource/{datasourceName}/details Access detailed datasource information including schema datasourceName (String) Lookup druid://lookup/{tier}/{lookupName} Access lookup configuration and data tier (String), lookupName (String) Segments druid://segment/{segmentId} Access segment metadata and information segmentId (String) Available Prompts by Feature Feature Prompt Name Description Parameters Data Analysis data-exploration Guide for exploring data in Druid datasources datasource (String, optional) Data Analysis query-optimization Help optimize Druid SQL queries for better performance query (String) Cluster Management health-check Comprehensive cluster health assessment guidance None Cluster Management cluster-overview Overview and analysis of cluster status None Ingestion Management ingestion-troubleshooting Troubleshoot ingestion issues issue (String, optional) Ingestion Management ingestion-setup Guide for setting up new ingestion pipelines dataSource (String, optional) Retention Management retention-management Manage data retention policies datasource (String, optional) Compaction compaction-suggestions Optimize segment compaction configuration datasource (String, optional), currentConfig (String, optional), performanceMetrics (String, optional) Compaction compaction-troubleshooting Troubleshoot compaction issues issue (String), datasource (String, optional) Operations emergency-response Emergency response procedures and guidance None Operations maintenance-mode Cluster maintenance procedures None

Configure your Druid connection in src/main/resources/application.properties:

# Spring AI MCP Server configuration
spring.ai.mcp.server.name=druid-mcp-server
spring.ai.mcp.server.version=1.0.0

# Druid configuration
druid.router.url=http://localhost:8888

# Server configuration
server.port=8080

# NOTE: Banner and console logging must be disabled for STDIO transport
spring.main.banner-mode=off

Environment Variables Configuration

For sensitive credentials like username and password, you can use environment variables instead of hardcoding them in properties files.

Supported Environment Variables

DRUID_AUTH_USERNAME: Druid authentication username
DRUID_AUTH_PASSWORD: Druid authentication password
DRUID_ROUTER_URL: Override the default Druid router URL
DRUID_SSL_ENABLED: Enable SSL/TLS support (true/false)
DRUID_SSL_SKIP_VERIFICATION: Skip SSL certificate verification (true/false)

SSL-Encrypted Cluster with Authentication

This section provides comprehensive guidance on connecting to SSL-encrypted Druid clusters with username and password authentication.

SSL-enabled Druid cluster with HTTPS endpoints
Valid username and password credentials for Druid authentication
SSL certificates properly configured (or ability to skip verification for testing)

Method 1: Environment Variables (Recommended for Production)

Set the following environment variables before starting the MCP server:

# Druid cluster URL with HTTPS
export DRUID_ROUTER_URL="https://your-druid-cluster.example.com:8888"

# Authentication credentials
export DRUID_AUTH_USERNAME="your-username"
export DRUID_AUTH_PASSWORD="your-password"

# SSL configuration
export DRUID_SSL_ENABLED="true"
export DRUID_SSL_SKIP_VERIFICATION="false"  # Use "true" only for testing

# Start the MCP server
java -jar target/druid-mcp-server-1.0.0.jar

Method 2: Application Properties

Update src/main/resources/application.properties:

# Druid cluster configuration
druid.router.url=https://your-druid-cluster.example.com:8888

# Authentication
druid.auth.username=your-username
druid.auth.password=your-password

# SSL configuration
druid.ssl.enabled=true
druid.ssl.skip-verification=false

Method 3: Runtime System Properties

Pass configuration as JVM system properties:

java -Ddruid.router.url="https://your-druid-cluster.example.com:8888" \
     -Ddruid.auth.username="your-username" \
     -Ddruid.auth.password="your-password" \
     -Ddruid.ssl.enabled=true \
     -Ddruid.ssl.skip-verification=false \
     -jar target/druid-mcp-server-1.0.0.jar

SSL Configuration Options

For production environments with valid SSL certificates:

export DRUID_ROUTER_URL="https://druid-prod.company.com:8888"
export DRUID_SSL_ENABLED="true"
export DRUID_SSL_SKIP_VERIFICATION="false"

The server will use the system's default truststore to validate SSL certificates.

Development/Testing SSL Setup

For development or testing with self-signed certificates:

export DRUID_ROUTER_URL="https://druid-dev.local:8888"
export DRUID_SSL_ENABLED="true"
export DRUID_SSL_SKIP_VERIFICATION="true"  # WARNING: Only for testing!

⚠️ Security Warning: Never use DRUID_SSL_SKIP_VERIFICATION=true in production environments as it disables SSL certificate validation.

The MCP server supports HTTP Basic Authentication with username and password:

Username: Set via DRUID_AUTH_USERNAME or druid.auth.username
Password: Set via DRUID_AUTH_PASSWORD or druid.auth.password

The credentials are automatically encoded using Base64 and sent with each request using the Authorization: Basic header.

Complete Example Configurations Example 1: Production Environment

#!/bin/bash
# Production configuration script

# Druid cluster settings
export DRUID_ROUTER_URL="https://druid.production.company.com:8888"

# Authentication
export DRUID_AUTH_USERNAME="druid-mcp-user"
export DRUID_AUTH_PASSWORD="secure-password-123"

# SSL settings (production)
export DRUID_SSL_ENABLED="true"
export DRUID_SSL_SKIP_VERIFICATION="false"

# Start MCP server
java -jar target/druid-mcp-server-1.0.0.jar

Example 2: Development Environment

#!/bin/bash
# Development configuration script

# Local Druid cluster with self-signed certificates
export DRUID_ROUTER_URL="https://localhost:8888"

# Test credentials
export DRUID_AUTH_USERNAME="admin"
export DRUID_AUTH_PASSWORD="admin123"

# SSL settings (development - skip verification)
export DRUID_SSL_ENABLED="true"
export DRUID_SSL_SKIP_VERIFICATION="true"

# Start MCP server
java -jar target/druid-mcp-server-1.0.0.jar

Example 3: MCP Client Configuration with SSL

Update your mcp-servers-config.json to include environment variables:

{
  "mcpServers": {
    "druid-mcp-server": {
      "command": "java",
      "args": [
        "-Dspring.ai.mcp.server.stdio=true",
        "-Dspring.main.web-application-type=none",
        "-Dlogging.pattern.console=",
        "-jar",
        "target/druid-mcp-server-1.0.0.jar"
      ],
      "env": {
        "DRUID_ROUTER_URL": "https://your-druid-cluster.example.com:8888",
        "DRUID_AUTH_USERNAME": "your-username",
        "DRUID_AUTH_PASSWORD": "your-password",
        "DRUID_SSL_ENABLED": "true",
        "DRUID_SSL_SKIP_VERIFICATION": "false"
      }
    }
  }
}

The server provides extensive prompt customization capabilities through the prompts.properties file located in src/main/resources/.

Prompt Configuration Structure

The prompts.properties file contains:

Global Settings: Enable/disable prompts and set watermarks
Feature Toggles: Control which prompts are available
Custom Variables: Organization-specific information
Template Definitions: Full prompt templates for each feature

You can override any prompt template using Java system properties with the -D flag:

Method 1: System Properties (Runtime Override)

java -Dprompts.druid-data-exploration.template="Your custom template here" \
     -jar target/druid-mcp-server-1.0.0.jar

Method 2: Custom Properties File

Create a custom properties file (e.g., custom-prompts.properties):

# Custom prompt template
prompts.druid-data-exploration.template=My custom data exploration prompt:\n\
1. Custom step one\n\
2. Custom step two\n\
{datasource_section}\n\
Environment: {environment}

Load it at runtime:

java -Dspring.config.additional-location=classpath:custom-prompts.properties \
     -jar target/druid-mcp-server-1.0.0.jar

Available Prompt Variables

All prompt templates support these variables:

Variable Description Example {environment} Current environment name production, staging, dev {organizationName} Organization name Your Organization {contactInfo} Contact information your-team@company.com {watermark} Generated watermark Generated by Druid MCP Server v1.0.0 {datasource} Datasource name (context-specific) sales_data {query} SQL query (context-specific) SELECT * FROM sales_data Custom Data Exploration Prompt

prompts.druid-data-exploration.template=Welcome to {organizationName} Druid Analysis!\n\n\
Please help me explore our data:\n\
{datasource_section}\n\
Environment: {environment}\n\
Contact: {contactInfo}\n\n\
{watermark}

Custom Query Optimization Prompt

prompts.druid-query-optimization.template=Query Performance Analysis for {organizationName}\n\n\
Query to optimize: {query}\n\n\
Please provide:\n\
1. Performance bottleneck analysis\n\
2. Optimization recommendations\n\
3. Best practices for our {environment} environment\n\n\
{watermark}

Disabling Specific Prompts

You can disable individual prompts by setting their enabled flag to false:

mcp.prompts.data-exploration.enabled=false
mcp.prompts.query-optimization.enabled=false

Or disable all prompts globally:

mcp.prompts.enabled=false

This server uses Spring AI's MCP Server framework and supports both STDIO and SSE transports. The tools, resources, and prompts are automatically registered and exposed through the MCP protocol.

STDIO Transport (Recommended for LLM clients)

java -Dspring.ai.mcp.server.stdio=true \
     -Dspring.main.web-application-type=none \
     -Dlogging.pattern.console= \
     -jar target/druid-mcp-server-1.0.0.jar

SSE Transport (HTTP-based)

java -jar target/druid-mcp-server-1.0.0.jar
# Server available at http://localhost:8080

MCP Configuration for LLMs

A ready-to-use MCP configuration file is provided at mcp-servers-config.json that can be used with LLM clients to connect to this Druid MCP server.

The configuration includes both transport options:

STDIO Transport (Recommended)

More details on this on examples/stdio/README.md.

{
  "mcpServers": {
    "druid-mcp-server": {
      "command": "java",
      "args": [
        "-Dspring.ai.mcp.server.stdio=true",
        "-Dspring.main.web-application-type=none",
        "-Dlogging.pattern.console=",
        "-jar",
        "target/druid-mcp-server-1.0.0.jar"
      ]
    }
  }
}

More details on this on examples/sse/README.md.

{
  "mcpServers": {
    "druid-mcp-server-sse": {
      "url": "http://localhost:8080"
    }
  }
}

Build the server first: See development.md for build instructions
For STDIO transport: The MCP server will be automatically started by the LLM client
For SSE transport: Start the server manually first

This repository includes comprehensive examples to help you get started with different deployment scenarios and transport modes:

Complete Docker Compose configuration for running a full Apache Druid cluster locally. Perfect for development, testing, and learning about Druid cluster architecture.

Features:

Full Druid cluster with all components (Coordinator, Broker, Historical, MiddleManager, Router)
PostgreSQL metadata storage and ZooKeeper coordination
Pre-configured with sample data and ingestion examples
Integrated Druid MCP Server for immediate testing

Configuration examples for STDIO (Standard Input/Output) transport mode - the recommended method for integrating with LLM clients like Claude Desktop.

Features:

Development and production configuration templates
Authentication and SSL setup examples
Integration guides for popular MCP clients
Troubleshooting and security best practices

Configuration examples for running the Druid MCP Server using Docker with STDIO transport mode. This approach combines the convenience of Docker deployment with STDIO transport for LLM client integration.

Features:

Docker-based MCP configuration files for development and production
No Java installation required on client machines
Docker Compose setup for simplified deployment
Environment variable configuration for Druid connections
Authentication and SSL support via Docker environment variables

Configuration examples for SSE (Server-Sent Events) transport mode, providing HTTP-based communication suitable for web applications and REST API integrations.

Features:

HTTP-based MCP server configuration
Custom port and production deployment examples
Web client integration patterns
Comparison with STDIO transport mode

This Druid MCP Server is part of a comprehensive ecosystem of Apache Druid tools and extensions developed by iunera. These complementary projects enhance different aspects of Druid cluster management and data ingestion:

Advanced configuration management and deployment tools for Apache Druid clusters. This project provides:

Automated Cluster Setup: Streamlined configuration templates for different deployment scenarios
Configuration Management: Best practices and templates for production Druid clusters
Deployment Automation: Tools and scripts for consistent cluster deployments
Environment-Specific Configs: Optimized configurations for development, staging, and production environments

Integration with Druid MCP Server: The cluster configurations provided by this project work seamlessly with the monitoring and management capabilities of the Druid MCP Server, enabling comprehensive cluster lifecycle management.

A specialized Apache Druid extension for ingesting and analyzing code-related data and metrics. This extension enables:

Code Metrics Ingestion: Specialized parsers for code analysis data and software metrics
Developer Analytics: Tools for analyzing code quality, complexity, and development patterns
CI/CD Integration: Seamless integration with continuous integration and deployment pipelines
Custom Data Formats: Support for various code analysis tools and formats

Integration with Druid MCP Server: This extension expands the ingestion capabilities that can be managed through the MCP server's ingestion management tools, providing specialized support for code analytics use cases.

Complete Ecosystem: From cluster setup to specialized data ingestion and management
Consistent Architecture: All projects follow similar design principles and integration patterns
Enhanced Capabilities: Each project extends different aspects of the Druid ecosystem
Production Ready: Battle-tested configurations and extensions for enterprise deployments

Readonly Mode: Implement a Readonly Mode (R) for Druid and disallow the Create, Update, Delete on all tools.
Authentication on SSE Mode: Introduce Oauth Authentication
Druid Auto Compaction: Intelligent automatic compaction configuration
MCP Auto Completion: Enhanced autocomplete functionality with sampling
Proper Observability: Comprehensive metrics and tracing
Enhanced Monitoring: Advanced cluster monitoring and alerting capabilities
Advanced Analytics: Machine learning-powered insights and recommendations
Security Enhancements: Advanced authentication and authorization features
Kubernetes Support: Proper deployment on Kubernetes

This Druid MCP Server is developed and maintained by iunera, a leading provider of advanced AI and data analytics solutions.

iunera specializes in:

AI-Powered Analytics: Cutting-edge artificial intelligence solutions for data analysis
Enterprise Data Platforms: Scalable data infrastructure and analytics platforms (Druid, Flink, Kubernetes, Kafka, Spring)
Model Context Protocol (MCP) Solutions: Advanced MCP server implementations for various data systems
Custom AI Development: Tailored AI solutions for enterprise needs

As veterans in Apache Druid iunera deployed and maintained a large number of solutions based on Apache Druid in productive enterprise grade scenarios.

For more information about our services and solutions, visit www.iunera.com.

Need help? Let

Website: https://www.iunera.com
Professional Services: Contact us through www.iunera.com or email for enterprise support and custom development
Open Source: This project is open source and community contributions are welcome

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