mcp-apache-spark-history-server MCP Server
kubeflow/mcp-apache-spark-history-server
[](https://agentrank-ai.com/tool/kubeflow--mcp-apache-spark-history-server/?utm_source=badge&utm_medium=readme&utm_campaign=agentrank_badge) MCP Server and CLI for Apache Spark History Server. Debug Spark applications from AI agents, scripts, or the terminal.
claude mcp add agentrank -- npx -y agentrank-mcp-server Overview
kubeflow/mcp-apache-spark-history-server is a Python MCP server licensed under Apache-2.0. MCP Server and CLI for Apache Spark History Server. Debug Spark applications from AI agents, scripts, or the terminal. Topics: apache-spark, kubernetes, mcp, mcp-server, big-data, data-processing.
Ranked #1187 out of 24846 indexed tools.
In the top 5% of all indexed tools.
Has 16 contributors.
Ecosystem
Score Breakdown
204 stars → growing community
Last commit 15d ago → recently updated
34/51 issues closed → decent issue management
16 contributors → active community
No dependents → no downstream usage
Weights: Freshness 20% · Issue Health 20% · Dependents 22% · Stars 10% · Contributors 8% · How we score →
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From the README
# Kubeflow Spark AI Toolkit > **Connect AI agents and engineers to Apache Spark History Server for intelligent job analysis, performance monitoring, and investigation** --- > [!IMPORTANT] > ### ✨ NEW — Spark History Server CLI is now available > > > A standalone Go binary that queries Spark History Server **directly from your terminal** — no MCP, no AI framework, no daemon process. Inspect jobs, compare runs, investigate failures, and script against the Spark REST API. > > **[Get started with the SHS CLI →](skills/cli/README.md)** --- This project provides **two interfaces** to your Spark History Server data: | | 🛠️ [SHS CLI (`shs`)](#️-shs-cli-shs--for-engineers--scripts) | ⚡ [MCP Server](#-mcp-server--for-ai-agents) | |---|---|---| | **For** | Engineers, shell scripts, CI/CD, coding agents | AI agents and MCP-compatible clients | | **Mental model** | "I know the command I want to run" | "Agent, investigate this Spark app" | | **Install** | Single static binary — no dependenciRead full README on GitHub →
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