MCP-Ambari-API

Sat, 02 May 2026 07:24:38 +0000

What is MCP-Ambari-API?

A Model Context Protocol (MCP) server for Apache Ambari that brings
natural language operations to Hadoop cluster management. Works with
Claude Desktop, Claude Code, OpenWebUI, or any MCP-compatible LLM client.

Stop juggling Ambari Web UI, curl commands, and SSH sessions. Just ask:

"Show me the HDFS DFSAdmin report"
"Restart YARN service in cluster TEST-AMBARI"
"Which DataNodes have high heap memory usage in the last 30 minutes?"
"List all current CRITICAL alerts grouped by service"
"Compare NameNode and DataNode JVM memory over the past hour"

Why I built it

Day-to-day Ambari operations — checking service status, inspecting
configs across host groups, querying AMS metrics, diagnosing alerts —
require constant context switching between the Web UI, CLI, and
documentation. I wanted an LLM agent that could translate operator
intent into precise Ambari REST API calls, with proper safety guards
for destructive operations.

Key Features

Service Operations: Start/stop/restart HDFS, YARN, Spark, HBase,
and other Hadoop services individually or in bulk
Configuration Management: Unified dump_configurations tool with
filtering, summarization, and bulk export across all config types
AMS Metrics Intelligence: Catalog-driven exact-match queries
against Ambari Metrics Service with auto-discovery of appIds and
metric names
HDFS DFSAdmin Reports: Generate hdfs dfsadmin -report-style
capacity and DataNode summaries through natural language
Alert Management: Unified tool for current and historical alerts
with state, scope, and time-range filtering
Multi-Cluster Support: Configure multiple Ambari environments and
switch between them via MCP server entries
Safety Guards: Bulk operations require confirmation, prompt
templates guide LLMs through risky commands
Bearer Token Auth: Production-ready authentication for
streamable-http mode
Flexible Transports: stdio for local Claude Desktop,
streamable-http for remote/Docker deployments

Installation

PyPI package available:

uvx --python 3.12 mcp-ambari-api

Claude Desktop config:

{
  "mcpServers": {
    "mcp-ambari-api": {
      "command": "uvx",
      "args": ["--python", "3.12", "mcp-ambari-api"],
      "env": {
        "AMBARI_HOST": "your-ambari-host",
        "AMBARI_PORT": "8080",
        "AMBARI_USER": "admin",
        "AMBARI_PASS": "admin",
        "AMBARI_CLUSTER_NAME": "TEST-AMBARI"
      }
    }
  }
}

Or run the full Docker Compose stack with MCP-Server, MCPO Proxy, and
OpenWebUI in 5 minutes.

Tech Stack

Python 3.12 + asyncio
FastMCP framework
Apache Ambari REST API (2.7+)
stdio and streamable-http transports
Available on PyPI, Docker Hub, and Smithery

Looking for Feedback

Which additional Ambari areas should I cover next? (Kerberos config?
Stack/repo management? Service-specific deep dives?)
How should destructive bulk operations (stop_all_services) be gated
beyond current confirmation prompts?
Anyone running this against large production clusters — performance
feedback on AMS metric queries welcome
Cross-version testing reports (Ambari 2.7 vs 3.0 edge cases)

Repo: https://github.com/call518/MCP-Ambari-API
PyPI: https://pypi.org/project/MCP-Ambari-API/
Smithery: https://smithery.ai/server/@call518/mcp-ambari-api
DeepWiki: https://deepwiki.com/call518/MCP-Ambari-API

⭐ Stars, issues, and PRs welcome. The codebase includes an
"Adding Custom Tools" guide for easy extension to your organization's
specific Hadoop monitoring needs.