monte-carlo-analyze-root-cause

Name: monte-carlo-analyze-root-cause
Availability: InStock
Author: Monte Carlo

Trace lineage and analyze Monte Carlo observability data to identify data incident root causes.

90|6|Updated Feb 25, 2026

Search Tags:#MCP #alerts #etl #investigate #root-cause #lineage #data-incident

Ready to run

npx skills add https://github.com/monte-carlo-data/mc-agent-toolkit --skill monte-carlo-analyze-root-cause

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: monte-carlo-analyze-root-cause
Source: https://github.com/monte-carlo-data/mc-agent-toolkit/tree/main/skills/analyze-root-cause
Command: npx skills add https://github.com/monte-carlo-data/mc-agent-toolkit --skill monte-carlo-analyze-root-cause

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

Investigate data incidents to identify root causes efficiently using Monte Carlo's observability data and MCP tools.

Core Features & Use Cases

Map blast radius by tracing upstream/downstream lineage.
Investigate incidents across freshness, volume, schema, ETL, and query issues.
Profile data when a database MCP is available.
Match findings against a catalog of known root-cause patterns.

Quick Start

Activate when user asks about data incidents; for immediate use, call the MCP tools to collect alerts, lineage, and ETL status and begin root-cause analysis.

Frequently Asked Questions about monte-carlo-analyze-root-cause

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I investigate a data incident and find the root cause?▼

You investigate a data incident by tracing upstream and downstream lineage, checking ETL status, and analyzing freshness metrics using Monte Carlo observability data to map the blast radius and identify the root cause.

What is the best way to diagnose freshness delays and volume anomalies?▼

Diagnosing freshness delays and volume anomalies involves querying Monte Carlo alerts and comparing findings against a catalog of known root-cause patterns to pinpoint ETL failures or field metric drift.

How does lineage tracing work for mapping the blast radius of schema changes?▼

Lineage tracing for schema changes maps upstream and downstream dependencies across datasets, revealing the full blast radius of the data incident to determine which downstream tables or dashboards are affected.

Do I need a Monte Carlo MCP server to investigate ETL failures?▼

Yes, you need access to the Monte Carlo MCP server to investigate ETL failures, as it provides the observability data and tools required to collect alerts, trace lineage, and perform change timeline analysis.

Can I profile data directly when investigating field metric drift?▼

Yes, you can profile data when investigating field metric drift if a database MCP is available, allowing you to query the underlying database and combine profiling results with change timeline analysis to confirm the root cause.

What types of data issues can be diagnosed using Monte Carlo observability data?▼

Monte Carlo observability data can diagnose data issues including freshness delays, volume anomalies, schema changes, ETL failures, and field metric drift across any dataset by matching findings against known root-cause patterns.