MCP Server for Apache Spark History Server. The bridge between Agentic AI and Apache Spark.
Config is the same across clients — only the file and path differ.
{
"mcpServers": {
"mcp-apache-spark-history-server": {
"command": "<see-readme>",
"args": []
}
}
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🤖 Connect AI agents to Apache Spark History Server for intelligent job analysis and performance monitoring
No automated test available for this server. Check the GitHub README for setup instructions.
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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.
This project provides two interfaces to your Spark History Server data:
🛠️ SHS CLI (shs) | ⚡ MCP Server | |
|---|---|---|
| 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 dependencies | Python 3.12+, uv |
| Get started | CLI docs → | MCP docs → |
graph TB
subgraph Clients
A[🤖 AI Agent / LLM]
B[👩💻 Engineer / Script / CI]
C[🔧 Coding Agent - Claude Code / Kiro]
end
subgraph "Kubeflow Spark AI Toolkit"
D[⚡ MCP Server]
E[🛠️ CLI - shs]
end
subgraph "Spark History Servers"
F[🔥 Production]
G[🔥 Staging / Dev]
end
A -->|MCP Protocol| D
B -->|Terminal commands| E
C -->|shs skill file| E
D -->|REST API| F
D -->|REST API| G
E -->|REST API| F
E -->|REST API| G
shs) — For Engineers & ScriptsA standalone Go binary — no MCP, no dependencies, no running daemon. Query your Spark History Server directly from the terminal, shell scripts, or CI/CD pipelines. Also works as a skill for coding agents like Claude Code and Kiro.
# Auto-detect latest version, OS, and architecture
VERSION=$(curl -s https://api.github.com/repos/kubeflow/mcp-apache-spark-history-server/releases | grep -m1 '"tag_name": "cli/' | cut -d'"' -f4 | sed 's|cli/||')
OS=$(uname -s | tr '[:upper:]' '[:lower:]')
ARCH=$(uname -m)
[ "$ARCH" = "x86_64" ] && ARCH="amd64"
[ "$ARCH" = "aarch64" ] && ARCH="arm64"
curl -sSL "https://github.com/kubeflow/mcp-apache-spark-history-server/releases/download/cli%2F${VERSION}/shs-${VERSION}-${OS}-${ARCH}.tar.gz" | tar xz
sudo mv shs /usr/local/bin/
# Generate a config file
shs setup config > config.yaml # then set your Spark History Server URL
# Explore applications
shs apps
shs jobs -a APP_ID --status failed
shs stages -a APP_ID --sort duration
shs compare apps --app-a APP1 --app-b APP2
# Use as a skill with Claude Code or Kiro
shs setup skill > ~/.claude/skills/spark-
... [View full README on GitHub](https://github.com/kubeflow/mcp-apache-spark-history-server#readme)