What features does MCP-Ambari-API MCP server provide?

Q: What features does MCP-Ambari-API MCP server provide?

Interactive Ambari Operations Hub: Provides an MCP-based foundation for querying and managing Ambari services through natural language instead of traditional consoles or UIs.. Real-time Cluster Visibility: Delivers a 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 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 via prompts.. 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.

MCP-Ambari-API MCP Server 2025: Features & Setup Guide

Overview

MCP Ambari API is a powerful 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, it provides an Interactive Ambari Operations Hub, real-time cluster visibility, and a metrics intelligence pipeline. The server supports two transport modes (stdio and streamable-http), Docker Compose deployment, and token-based authentication for secure remote access. It consolidates repetitive operations into automated workflows, offers built-in reports (such as DFSAdmin-style capacity and service summaries), and includes safety guards and guardrails that require user confirmations for large-scale actions. The architecture emphasizes asynchronous HTTP, structured logging, AMS metadata caching, and modular tool layers to facilitate easy extension. Core features cover cluster/service/config/host/user/alert management and a suite of MCP tools for querying and operating the Ambari cluster. Deployment is production-validated and available via PyPI, Docker images, and other channels, with extensive guidance for Ambari REST integration, AMS metric querying, and OpenWebUI/OpenAPI workflows.

Features

Interactive Ambari Operations Hub

Provides an MCP-based foundation for querying and managing Ambari services through natural language instead of traditional consoles or UIs.

Real-time Cluster Visibility

Delivers a 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 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 via prompts.

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.