mcp-apache-spark-history-server MCP server

MCP Server and CLI for Apache Spark History Server. Debug Spark applications from AI agents, scripts, or the terminal.

What mcp-apache-spark-history-server does

Summarised from the project README; check the repository for the latest details.

Tools exposed (19)

• list_applications – List applications with optional status, date, and limit filters; pass `appid` for a single application's detail (status, resources, duration
• 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

How to install mcp-apache-spark-history-server

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