Ops MCP Server

Read the Spark driver log from S3 for an EMR Serverless job run.

DEFAULT: Reads stdout.gz — this is the PRIMARY log containing Python print statements, row counts, file paths, and application errors. This is what you want 90% of the time.

Use log_type='stderr' only when you need Spark framework logs (executor allocation, memory warnings, shuffle errors).

Use read_both=True to get BOTH logs in one call (stdout first, then stderr filtered to ERROR lines only).

How to find application_id and job_run_id:

• application_id: from the 'initialise' Airflow task log → 'EMR serverless application created: 00gXXX'

• job_run_id: from the processing Airflow task log → 'EMR serverless job started: 00gXXX'

• Or use list_emr_applications() then list_job_runs()

Args: application_id: The EMR Serverless application ID (e.g. '00g16i3marao0c0t'). job_run_id: The job run ID (e.g. '00g16i5g2pm56o0v'). log_type: 'stdout' (default, Python app output) or 'stderr' (Spark framework logs). s3_log_uri: Optional full S3 URI to read directly (e.g. 's3://bucket/path/stdout.gz'). process_name: Optional folder name under spark-logs/ (e.g. 'stackadapt_main'). Speeds up log discovery. tail_lines: Number of lines from the end (default 300). Use -1 for all lines. search_text: Optional text to filter log lines (e.g. 'ERROR', 'Exception'). bucket: S3 bucket override (default from config). read_both: If True, read BOTH stdout and stderr in one call. stdout shown first, stderr filtered to ERROR lines.

Returns the log content, optionally filtered and tailed.