Bundled upstream support

Supporting files are included at skill-relative paths. See UPSTREAM-NOTICE.md for their pinned sources, licenses, hashes, and import changes.

Monte Carlo Performance Diagnosis Skill

This skill helps diagnose data pipeline performance issues using Monte Carlo's cross-platform observability data. It works across Airflow, dbt, Databricks, and warehouse query engines to find bottlenecks, detect regressions, and identify root causes.

Monte Carlo tool routing (required): Always call Monte Carlo MCP tools through this plugin's bundled server, whose fully-qualified tool names are mcp__plugin_mc-agent-toolkit_monte-carlo-mcp__<tool> (e.g. mcp__plugin_mc-agent-toolkit_monte-carlo-mcp__get_alerts). Bare tool names used in this skill (get_alerts, search, get_table, …) refer to that bundled server. If the session also has a separately-configured monte-carlo-mcp server, do not route to it — it may point at a different endpoint or credentials.

Reference files live next to this skill file. Use the Read tool (not MCP resources) to access them:

• Tiered investigation approach: references/investigation-tiers.md (relative to this file)
• Query analysis patterns: references/query-analysis.md (relative to this file)

When to activate this skill

Activate when the user:

• Asks about slow pipelines, jobs, or queries
• Wants to find expensive or costly queries
• Mentions performance regressions or degradation
• Asks "why is this pipeline slow?" or "what's using the most compute?"
• Wants to compare performance over time or find bottleneck tasks
• Asks about failed or futile query patterns

When NOT to activate this skill

Do not activate when the user is:

• Investigating data quality issues (use the prevent skill)
• Looking at storage costs (use the storage-cost-analysis skill)
• Creating monitors (use the monitoring-advisor skill)
• Just querying data or exploring table contents

Prerequisites

The following MCP tools must be available (connect to Monte Carlo's MCP server):

Discovery tools (Tier 1):

• get_jobs_performance -- find slow/failing jobs across Airflow, dbt, Databricks
• get_top_slow_queries -- find slowest query groups by total runtime

Bridge tool:

• get_tables_for_job -- convert job MCONs to table MCONs

Diagnosis tools (Tier 2):

• get_tasks_performance -- drill into a job's individual tasks
• get_change_timeline -- unified timeline of query changes, volume shifts, Airflow/dbt failures
• get_query_rca -- root cause analysis for failed/futile queries
• get_query_latency_distribution -- latency trend over time
• get_asset_lineage -- trace upstream/downstream impact

Supporting tools:

• get_warehouses -- list available warehouses

Workflow

Step 1: Identify the scope

Determine what the user wants to investigate:

• Specific job/pipeline: User mentions a job name or pipeline
• Specific table: User mentions a table that's slow to update
• General discovery: User wants to find what's slow

Call get_warehouses to list available warehouses. Match the user's context to a warehouse.

Step 2: Tier 1 -- Discovery

If you don't have specific MCONs to investigate, start with discovery:

1. Find slow jobs: Call get_jobs_performance with optional integration_type filter (AIRFLOW, DATABRICKS, DBT) if the user specifies a platform.

   • Results include: job name, average duration, trend (7-day), run count, failure rate
   • Look for: high avgDuration, negative runDurationTrend7d, high failure rates

2. Find expensive queries: Call get_top_slow_queries with optional warehouse_id and query_type ("read" for SELECTs, "write" for INSERT/CREATE/MERGE).

   • Results include: query hash, total runtime, average runtime, run count
   • Look for: queries with high total runtime or high individual execution time

Present the top findings to the user before drilling deeper. A typical investigation needs only 3-7 tool calls.

If both discovery tools return no results: Tell the user no performance issues were found in the current time window. Suggest broadening the scope (different warehouse, longer time range, or a different platform filter).

Step 3: Bridge -- Job to Tables

After Tier 1 identifies problematic jobs, convert to table MCONs:

…