| name | monte-carlo-performance-diagnosis |
| description | Diagnoses pipeline performance issues -- slow jobs, expensive queries, latency trends -- using Monte Carlo's cross-platform observability. Uses a tiered investigation approach: discover problems, bridge to affected tables, then drill into root causes. Activates when a user asks about... |
| risk | unknown |
| source | https://github.com/monte-carlo-data/mc-agent-toolkit/tree/main/skills/performance-diagnosis |
| source_repo | monte-carlo-data/mc-agent-toolkit |
| source_type | community |
| date_added | 2026-07-01T00:00:00.000Z |
| license | Apache-2.0 |
| license_source | https://github.com/monte-carlo-data/mc-agent-toolkit/blob/main/LICENSE |
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, Databricksget_top_slow_queries -- find slowest query groups by total runtime
Bridge tool:
get_tables_for_job -- convert job MCONs to table MCONs
