by kubeflow · MCP Server · ★ 188
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Kubeflow Spark History MCP Server 🤖 Connect AI agents to Apache Spark History Server for intelligent job analysis and performance monitoring Transform your Spark infrastructure monitoring with AI! This Model Context Protocol (MCP) server enables AI agents to analyze job performance, identify bottlenecks, and provide intelligent insights from your Spark History Server data. 🎯 What is This? Spark History Server MCP bridges AI agents with your existing Apache Spark infrastructure, enabling: 🔍 Query job details through natural language 📊 Analyze performance metrics across applications 🔄 Compare multiple jobs to identify regressions 🚨 Investigate failures with detailed error analys
| Stars | 188 |
| Forks | 67 |
| Language | Python |
| Category | MCP Server |
| License | Apache-2.0 |
| Quality Score | 71.4332806144269/100 |
| Open Issues | 19 |
| Last Updated | 2026-08-07 |
| Created | 2025-06-26 |
| Platforms | cli, k8s, mcp, python |
| Est. Tokens | ~17k |
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mcp-apache-spark-history-server is MCP Server and CLI for Apache Spark History Server. Debug Spark applications from AI agents, scripts, or the terminal.. It is categorized as a MCP Server with 188 GitHub stars.
mcp-apache-spark-history-server is primarily written in Python. It covers topics such as apache-spark, big-data, data-processing.
You can find installation instructions and usage details in the mcp-apache-spark-history-server GitHub repository at github.com/kubeflow/mcp-apache-spark-history-server. The project has 188 stars and 67 forks, indicating an active community.
mcp-apache-spark-history-server is released under the Apache-2.0 license, making it free to use and modify according to the license terms.
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