MCP Ambari API - Apache Hadoop Cluster Management Automation

🚀 Automate Apache Ambari operations with AI/LLM: Natural language commands for Hadoop cluster management, service control, configuration monitoring, and real-time status tracking via Model Context Protocol (MCP) tools.

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📋 Overview

MCP Ambari API is a powerful Model Context Protocol (MCP) server that enables seamless Apache Ambari cluster management through natural language commands. Built for DevOps engineers, data engineers, and system administrators who work with Hadoop ecosystems.

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🎯 What You Can Do

• Automated Service Management: Start, stop, restart Hadoop services (HDFS, YARN, Spark, etc.) with simple commands
• Real-time Monitoring: Monitor cluster health, service status, and performance metrics
• Configuration Management: View, update, and manage cluster configurations across all services
• Alert Management: Track and manage cluster alerts and notifications
• User & Host Management: Manage cluster users, permissions, and host assignments
• Request Tracking: Monitor long-running operations with detailed progress tracking

Docuement for Airflow REST-API

• Ambari API Documents

Topics

apache-ambari hadoop-cluster mcp-server cluster-automation devops-tools big-data infrastructure-management ai-automation llm-tools python-mcp

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Example Queries - Cluster Info/Status

Go to More Example Queries

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Dev Env

• WSL2(networkingMode = bridged) + Docker-Desktop

  • .wslconfig: tested with networkingMode = bridged

• Python 3.11 venv

  ### Option-1: with uv
  uv venv --python 3.11 --seed
  
  ### Option-2: with pip
  python3.11 -m venv .venv
  source .venv/bin/activate
  pip install -U pip
  

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🚀 QuickStart Guide /w Docker

Note: The following instructions assume you are using the streamable-http mode for MCP Server.

Env

• Ambari-3.0 Cluster

1. Prepare Ambari Cluster (Test Target)

To set up a Ambari Demo cluster, follow the guide at: Install Ambari 3.0 with Docker

2. Run Docker-Compose

Start the MCP-Server, MCPO(MCP-Proxy for OpenAPI), and OpenWebUI.

1. Ensure Docker and Docker Compose are installed on your system.

2. Clone this repository and navigate to its root directory.

3. Check docker-compose.yml and update.

4. Check mcp-config.json.http and update.

5. Check Networking for Host and Docker Containers.

6. Run:

   docker-compose up -d
   

• OpenWebUI will be available at the port specified in your docker-compose.yml.
  • e.g: http://localhost:3001 or as configured.
• The MCPO-Proxy will be accessible for API requests and cluster management, and its port is also specified in your docker-compose.yml.
  • e.g: 8001 or as configured.
• The list of MCP tool features provided by swagger can be found in the MCPO API Docs URL.
  • e.g: http://localhost:8001/ambari-api/docs

3. Registering the Tool in OpenWebUI

1. logging in to OpenWebUI with an admin account
2. go to "Settings" → "Tools" from the top menu.
3. Enter the ambari-api Tool address (e.g., http://localhost:8000/ambari-api) to connect MCP Tools with your Ambari cluster.

4. More Examples: Using MCP Tools to Query Ambari Cluster

Below is an example screenshot showing how to query the Ambari cluster using MCP Tools in OpenWebUI:

Example Query - Cluster Configuration Review & Recommendations

Example Query - Restart HDFS Service

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🔧 Usage & Configuration

This MCP server supports two connection modes: stdio (traditional) and streamable-http (Docker-based). You can configure the transport mode using CLI arguments or environment variables.

Configuration Priority: CLI arguments > Environment variables > Default values

CLI Arguments

• --type (-t): Transport type (stdio or streamable-http) - Default: stdio
• --host: Host address for HTTP transport - Default: 127.0.0.1
• --port (-p): Port number for HTTP transport - Default: 8080

Environment Variables

• FASTMCP_TYPE: Transport type (stdio or streamable-http)

• FASTMCP_HOST: Host address for HTTP transport

• FASTMCP_PORT: Port number for HTTP transport (also enables streamable-http mode when set)

• AMBARI_PORT: Port number for the Ambari server (default: 8080)

• AMBARI_USER: Username for Ambari server authentication (e.g., "admin")

• AMBARI_PASS: Password for Ambari server authentication (e.g., "admin")

• AMBARI_CLUSTER_NAME: Name of the target Ambari cluster (e.g., "TEST-AMBARI")

• AMBARI_LOG_LEVEL: Logging level for the MCP server (DEBUG, INFO, WARNING, ERROR)

Transport Selection(Priority) Logic:

• CLI Args: --type streamable-http --host 0.0.0.0 --port 18002
• Environment Variables: FASTMCP_TYPE=streamable-http FASTMCP_HOST=0.0.0.0 FASTMCP_PORT=18002
• Default Values: stdio mode when no configuration is provided
  • --type's default stdio
  • --host's default 127.0.0.1
  • --port's default 8080

Using this is very simple and straightforward. If you already have an MCP Tools environment running, just add the following configuration to your mcp-config.json file:

Method 1: Local MCP (transport="stdio")

{
  "mcpServers": {
    "ambari-api": {
      "command": "uvx",
      "args": ["--python", "3.11", "mcp-ambari-api"],
      "env": {
        "AMBARI_HOST": "host.docker.internal",
        "AMBARI_PORT": "8080",
        "AMBARI_USER": "admin",
        "AMBARI_PASS": "admin",
        "AMBARI_CLUSTER_NAME": "TEST-AMBARI",
        "AMBARI_LOG_LEVEL": "INFO"
      }
    }
  }
}

Method 2: Remote MCP (transport="streamable-http")

On MCP-Server Host:

# Ambari connection settings
export AMBARI_HOST="127.0.0.1"
export AMBARI_PORT="8080"
export AMBARI_USER="admin"
export AMBARI_PASS="admin"
export AMBARI_CLUSTER_NAME="TEST-AMBARI"
export AMBARI_LOG_LEVEL="INFO"

# MCP transport settings (choose one method)
# Method A: Using environment variables
export FASTMCP_TYPE="streamable-http"
export FASTMCP_HOST="0.0.0.0" 
export FASTMCP_PORT="8080"

# Method B: Using CLI arguments
uvx mcp-ambari-api --type streamable-http --host 0.0.0.0 --port 8080

On MCP-Client Host:

{
  "mcpServers": {
    "ambari-api": {
      "type": "streamable-http",
      "url": "http://localhost:8080/mcp"
    }
  }
}

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🎯 Core Features & Capabilities

Service Operations

• Hadoop Service Management: Start, stop, restart HDFS, YARN, Spark, HBase, and more
• Bulk Operations: Control all cluster services simultaneously
• Status Monitoring: Real-time service health and performance tracking

Configuration Management

• Unified Config Tool: Single interface for all configuration types (yarn-site, hdfs-site, etc.)
• Bulk Configuration: Export and manage multiple configurations with filtering
• Configuration Validation: Syntax checking and validation before applying changes

Monitoring & Alerting

• Real-time Alerts: Current and historical cluster alerts with filtering
• Request Tracking: Monitor long-running operations with detailed progress
• Host Monitoring: Hardware metrics, component states, and resource utilization

Administration

• User Management: Check cluster user administration
• Host Management: Node registration, component assignments, and health monitoring

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Available MCP Tools

This MCP server provides the following tools for Ambari cluster management:

Cluster Management

• get_cluster_info - Retrieve basic cluster information and status
• get_active_requests - List currently active/running operations
• get_request_status - Check status and progress of specific requests

Service Management

• get_cluster_services - List all services with their status
• get_service_status - Get detailed status of a specific service
• get_service_components - List components and host assignments for a service
• get_service_details - Get comprehensive service information
• start_service - Start a specific service
• stop_service - Stop a specific service
• restart_service - Restart a specific service
• start_all_services - Start all services in the cluster
• stop_all_services - Stop all services in the cluster
• restart_all_services - Restart all services in the cluster

Configuration Tools

• dump_configurations - Unified configuration tool (replaces get_configurations, list_configurations, and the former internal dump_all_configurations). Supports:
  • Single type: dump_configurations(config_type="yarn-site")
  • Bulk summary: dump_configurations(summarize=True)
  • Filter by substring (type or key): dump_configurations(filter="memory")
  • Service filter (narrow types by substring): dump_configurations(service_filter="yarn", summarize=True)
  • Keys only (no values): dump_configurations(include_values=False)
  • Limit number of types: dump_configurations(limit=10, summarize=True)

Breaking Change: get_configurations and list_configurations were removed in favor of this single, more capable tool.

Host Management

• list_hosts - List all hosts in the cluster
• get_host_details - Get detailed information for specific or all hosts (includes component states, hardware metrics, and service assignments)

User Management

• list_users - List all users in the Ambari system with their usernames and API links
• get_user - Get detailed information about a specific user including:
  • Basic profile (ID, username, display name, user type)
  • Status information (admin privileges, active status, login failures)
  • Authentication details (LDAP user status, authentication sources)
  • Group memberships, privileges, and widget layouts

Alert Management

• get_alerts_history - Unified alert tool for both current and historical alerts:
  • Current mode (mode="current"): Retrieve current/active alerts with real-time status
    • Current alert states across cluster, services, or hosts
    • Maintenance mode filtering (ON/OFF)
    • Summary formats: basic summary and grouped by definition
    • Detailed alert information including timestamps and descriptions
  • History mode (mode="history"): Retrieve historical alert events from the cluster
    • Scope filtering: cluster-wide, service-specific, or host-specific alerts
    • Time range filtering: from/to timestamp support
    • Pagination support for large datasets
  • Common features (both modes):
    • State filtering: CRITICAL, WARNING, OK, UNKNOWN alerts
    • Definition filtering: filter by specific alert definition names
    • Multiple output formats: detailed, summary, compact
    • Unified API for consistent alert querying experience

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🤝 Contributing & Support

How to Contribute

• 🐛 Report Bugs: GitHub Issues
• 💡 Request Features: Feature Requests
• 🔧 Submit PRs: Contributing Guidelines
• 📖 Improve Docs: Help make documentation better

Technologies Used

• Language: Python 3.11
• Framework: Model Context Protocol (MCP)
• API: Apache Ambari REST API
• Transport: stdio (local) and streamable-http (remote)
• Deployment: Docker, Docker Compose, PyPI

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❓ Frequently Asked Questions

Q: What Ambari versions are supported?

A: Ambari 2.7+ is recommended. Earlier versions may work but are not officially tested.

Q: Can I use this with cloud-managed Hadoop clusters?

A: Yes, as long as Ambari API endpoints are accessible, it works with on-premise, cloud, and hybrid deployments.

Q: How do I troubleshoot connection issues?

A: Check your AMBARI_HOST, AMBARI_PORT, and network connectivity. Enable debug logging with AMBARI_LOG_LEVEL=DEBUG.

Q: How does this compare to Ambari Web UI?

A: This provides programmatic access via AI/LLM commands, perfect for automation, scripting, and integration with modern DevOps workflows.

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📄 License

This project is licensed under the MIT License.