Connect AI agents and engineers to Apache Spark History Server for intelligent job analysis, performance monitoring, and investigation
Apache Spark History is an MCP project maintained in the kubeflow/mcp-apache-spark-history-server repository. The maintainer describes its purpose as “Connect AI agents and engineers to Apache Spark History Server for intelligent job analysis, performance monitoring, and investigation”. Read the project documentation for the complete scope.
Project documentation · Repository · Discovery listing. This profile records the public documentation checked on 2026-10-08.
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| Tool | What it does |
|---|---|
| list_applications | List applications with optional status, date, and limit filters; pass app_id for a single application's detail (status, resources, duration, attempts).… |
| list_jobs | List jobs with status/job-id filtering and sorting (e.g. slowest by duration) |
| list_stages | List stages with status filtering and sorting (e.g. slowest by duration) |
| get_stage | Stage detail with attempt and task metric distributions |
| list_stage_task_failures | Failed tasks of a stage with their error messages (exception/stack trace) |
| list_executors | List executors with executor-id filtering and sorting (failed-tasks/duration/gc/id) |
| get_executor_summary | Aggregate metrics across all executors |
| get_resource_usage_timeline | Chronological executor add/remove with resource totals |
| get_executor_thread_dump | JVM thread dump for a driver/executor, with state/name/blocked filters (running apps only) |
| get_environment | Spark config, JVM info, system properties, classpath; optional section filter to return a single part |
| list_sql_executions | List SQL executions as curated summaries, with status/description filters, sorting, and a default limit |
| get_sql_execution | SQL execution header by default; opt-in plan, node metrics, job summaries, aggregated stage metrics, and stage list |
| compare_sql_executions | Compare aggregated performance metrics (stages, tasks, shuffle, spill, GC) between two SQL executions; opt-in plan-structure diff |
| get_job_bottlenecks | Identify bottlenecks across stages, tasks, and executors |
| compare_job_environments | Diff Spark configs between two applications |
| compare_job_performance | Diff performance metrics between two applications |
| compare_stages | Compare two stages (optionally across applications): stage metrics and per-task p25/p50/p75/max quantiles |
| aws_analyze_spark_workload | One-shot root cause analysis of failed/slow Spark workloads |
| aws_spark_code_recommendation | Code fix recommendations for identified Spark issues |
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