MCP-Ambari-API

by call518

GitHub 1 480 uses Remote

About

MCP-Ambari-API enables conversational management of Apache Ambari Hadoop clusters through AI assistants, providing natural language interfaces for cluster operations, monitoring, and configuration management without requiring manual console access. Key capabilities: - Service lifecycle control: start, stop, and restart services across Hadoop clusters - Real-time cluster visibility: monitor service health, host status, alerts, and ongoing requests - Ambari Metrics querying: discover AMS appIds and retrieve precise time-series metrics using exact identifiers - Configuration management: inspect and update cluster configurations directly through conversation - Operational reporting: generate HDFS status reports, service summaries, and capacity metrics - Safety guardrails: built-in confirmation requirements for large-scale operations to prevent accidental disruptions

README

MCP Ambari API - Apache Hadoop Cluster Management Automation

> 🚀 Automate Apache Ambari operations with AI/LLM: Conversational control for Hadoop cluster management, service monitoring, configuration inspection, and precise Ambari Metrics queries via Model Context Protocol (MCP) tools.

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[](https://opensource.org/licenses/MIT)

[](https://smithery.ai/server/@call518/mcp-ambari-api) [](https://mseep.ai/app/2fd522d4-863d-479d-96f7-e24c7fb531db) [](https://www.buymeacoffee.com/call518)

[](https://github.com/call518/MCP-Ambari-API/actions/workflows/pypi-publish.yml)

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Architecture & Internal (DeepWiki)

[](https://deepwiki.com/call518/MCP-Ambari-API)

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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.

Features

✅ Interactive Ambari Operations Hub – Provides an MCP-based foundation for querying and managing services through natural language instead of console or UI interfaces.

✅ Real-time Cluster Visibility – Comprehensive view of key metrics including service status, host details, alert history, and ongoing requests in a single interface.

✅ Metrics Intelligence Pipeline – Dynamically discovers and filters AMS appIds and metric names, connecting directly to time-series analysis workflows.

✅ Automated Operations Workflow – Consolidates repetitive start/stop operations, configuration checks, user queries, and request tracking into consistent scenarios.

✅ Built-in Operational Reports – Instantly delivers dfsadmin-style HDFS reports, service summaries, and capacity metrics through LLM or CLI interfaces.

✅ Safety Guards and Guardrails – Requires user confirmation before large-scale operations and provides clear guidance for risky commands through prompt templates.

✅ LLM Integration Optimization – Includes natural language examples, parameter mapping, and usage guides to ensure stable AI agent operations.

✅ Flexible Deployment Models – Supports stdio/streamable-http transport, Docker Compose, and token authentication for deployment across development and production environments.

✅ Performance-Oriented Caching Architecture – Built-in AMS metadata cache and request logging ensure fast responses even in large-scale clusters.

✅ Scalable Code Architecture – Asynchronous HTTP, structured logging, and modularized tool layers enable easy addition of new features.

✅ Production-Validated – Based on tools validated in test Ambari clusters, ready for immediate use in production environments.

✅ Diversified Deployment Channels – Available through PyPI packages, Docker images, and other preferred deployment methods.

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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🚀 QuickStart Guide /w Docker

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

Flow Diagram of Quickstart/Tutorial

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

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