call518/mcp-ambari-api

이 페이지는 AI로 자동 번역되었습니다. 기술적인 세부 사항은 원문을 확인하세요. 원문 보기

1 stars 6 forks 품질 82 트렌드 25MIT

🚀 Natural-language Hadoop cluster management via MCP. Connect LLM agents (Claude, GPT, etc.) directly to Apache Ambari REST API — control services, inspect configs, query metrics, and manage alerts...

개요

: Conversational control for Hadoop cluster management, service monitoring, configuration inspection, and precise Ambari Metrics queries via Model Context Protocol (MCP) tools. 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. - ✅ – Provides an MCP-based foundation for querying and managing services through natural language instead of console or UI interfaces. - ✅ – Comprehensive view of key metrics including service status, host details, alert history, and ongoing requests in a single interface. - ✅ – Dynamically discovers and filters AMS appIds and metric names, connecting directly to time-series analysis workflows. - ✅ – Consolidates repetitive start/stop operations, configuration checks, user queries, and request tracking into consistent scenarios.

README

원문 README의 일부입니다. 원문 보기.

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.

Architecture & Internal (DeepWiki)

📋 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.

GitHub에서 전체 README 보기

설치

uvx --python 3.12 mcp-ambari-api

MCP 초보자라면? mcpskill.org 가이드부터 시작하세요 →

설정

{
  "mcpServers": {
    "mcp-ambari-api": {
      "type": "streamable-http",
      "url": "http://your-server:8000/mcp",
      "headers": {
        "Authorization": "Bearer your-secure-secret-key-here"
      }
    }
  }
}