by kubeflow · MCP Server · ★ 206
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
git clone https://github.com/kubeflow/mcp-apache-spark-history-server.git
uvx --from mcp-apache-spark-history-server spark-mcpTaken from the project's README on GitHub.
None of the 42 rules matched — no credential harvesting, no data exfiltration, no curl-pipe-shell installer. Scanned against 42 published rules built on the SlowMist agent-security taxonomy, refreshed every 8 hours.
The quality score combines nine heuristic checks of the repo and its README: completeness, clarity, specificity, examples, structure, agent readiness, step-by-step content, instruction quality and security notes. It rates how well the project explains itself to an agent, not the code.
| Stars | 206 |
| Forks | 78 |
| Language | Python |
| Category | MCP Server |
| License | Apache-2.0 |
| Quality Score | 71/100 |
| Open Issues | 15 |
| Last Updated | 2026-10-20 |
| Created | 2025-06-26 |
| Platforms | cli, k8s, mcp, python |
| Est. Tokens | ~17k |
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.
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Grades come from a rule-based scan built on the SlowMist agent-security taxonomy: 42 published rules, covering credential harvesting, data exfiltration, curl | sh installers and more. It is a first-layer scan, not a manual audit — we say so rather than overstate it.
The scale of the problem is documented independently: Liu et al. (2026), in a study of 31,132 agent skills, report that 26.1% contain security vulnerabilities. Our own full-catalog census is published as a citable open dataset.
Sources & who's responsible: