Triage Monte Carlo alerts interactively or build an automated workflow. Fetch, score, and troubleshoot alerts using MCP tools now, or design a reusable workflow that runs on a schedule.
日本語の概要は準備中です。原文の説明を表示しています。
Orchestrate incident response — triage, root cause, remediate, prevent recurrence. USE WHEN active alerts, data broken, stale, pipeline failure, or investigate and fix a data incident.
インストールする前に、エージェントに与えられる指示の中身を確認できます。
This workflow orchestrates the full lifecycle of a data incident by sequencing existing Monte Carlo skills. It does not contain investigation or remediation logic itself — each step loads the relevant skill's SKILL.md which has the actual instructions.
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-configuredmonte-carlo-mcpserver, do not route to it — it may point at a different endpoint or credentials.
Activate when:
/mc-incident-responseprevent skill (auto-activates via hooks)asset-health directlyStep 1 (conditional): Triage — when user has multiple/unknown alerts
Step 2: Root Cause Analysis — the core investigation
Step 3: Remediation — fix or escalate
Step 4 (optional): Prevent Recurrence — add monitoring
Before starting, determine which step to enter based on the user's context:
alert_types starting with "Agent ", or the user's issue is about an AI agent) → for the investigation, read ../troubleshoot-agent-traces/SKILL.md instead of ../analyze-root-cause/SKILL.md; the remediation and monitoring steps still applySkill: Read and follow ../automated-triage/SKILL.md
Goal: Fetch recent alerts, score them by confidence and impact, identify which ones need investigation.
When to run: Only when the user doesn't already have a specific alert or incident to investigate. This step helps narrow down "I have alerts" into "these specific alerts need attention."
Scope MCP calls tightly. On large accounts, broad queries return hundreds of results, overflow the tool-result token limit, spill to disk, and force chunk reads — burning user tokens and exhausting the turn budget. Minimum scoping for tools this workflow touches:
get_alerts → time filter (created_after, default last 7 days) + at least one of warehouse, table_names, severitysearch → needed to resolve a table name to its MCON (get_table requires MCON). Always pass limit (e.g. 5), the table name as query, and filter by warehouse_uuid or database/schema. warehouse_types alone is too broad. If multiple matches return: (1) auto-pick the match whose warehouse_display_name matches the user's named warehouse — do NOT stop to ask; (2) failing that, prefer the is_key_asset: true match; (3) only ask the user when none of these resolve itget_monitors → filter by mcons or warehouse_uuidIf scope is missing, ask the user before calling: "Which warehouse?", "How far back — today, this week?", "Any specific severity?".
Transition to Step 2: Once high-priority alert(s) are identified, tell the user:
"I've identified [N] high-priority alerts. Let me investigate the root cause of [specific alert/table]. Moving to root cause analysis."
Then proceed to Step 2 with the identified alert context.
Skill: Read and follow ../analyze-root-cause/SKILL.md
Goal: Investigate why the issue occurred — trace lineage, check ETL changes, analyze query modifications, profile data.
This is the core step. Most workflow entries start here.
Investigate linearly — do not re-call tools. Walk through the investigation once: (1) find the table, (2) fetch its alerts and freshness, (3) check lineage, (4) check recent queries/ETL. Call each tool at most once per table. If a tool result is insufficient, move to the next signal rather than re-calling with different params — burning turns on redundant calls exhausts the budget before the root cause is reached.
Transition to Step 3: When the root cause is identified (or the investigation reaches its limit), summarize findings and tell the user:
"Root cause identified: [summary]. Would you like me to help remediate this, or is the investigation sufficient?"
If the user wants to proceed, move to Step 3. If they say "that's enough", stop.
Skill: Read and follow ../remediation/SKILL.md
Goal: Fix the issue using available tools, or escalate with full context if the fix requires actions outside the agent's capability.
Transition to Step 4: After remediation is complete (fix applied or escalation documented), offer prevention:
"The issue has been [fixed/escalated]. The root cause was [X]. Want me to help add a monitor to detect this type of issue earlier next time?"
If the user says yes, move to Step 4. If no, the workflow is complete.
Skill: Read and follow ../monitoring-advisor/SKILL.md
When loading monitoring-advisor for this step, frame the request as direct monitor creation — not coverage analysis. The user already knows what they want to monitor (the thing that just broke). Example framing:
"Based on the incident, I recommend adding a [freshness/volume/validation] monitor on [table]. Let me create the monitor configuration."
Goal: Add or update a monitor to catch this class of issue in the future.
Do not force this step. It is optional — offer it after remediation, and respect if the user declines.
まだレビューはありません。使ってみた感想をお寄せください。
概要と使いどころ
Triage Monte Carlo alerts interactively or build an automated workflow. Fetch, score, and troubleshoot alerts using MCP tools now, or design a reusable workflow that runs on a schedule.
日本語の概要は準備中です。原文の説明を表示しています。
Build a Connection Auth Rules for a Monte Carlo connection type. Fetches live connector schemas and transform steps from the apollo-agent repo.
日本語の概要は準備中です。原文の説明を表示しています。
Generate SQL validation notebooks for dbt changes. Pass a GitHub PR URL or local dbt repo path.
日本語の概要は準備中です。原文の説明を表示しています。
Investigate data incidents and find root causes using Monte Carlo's observability data. Guides the agent through systematic investigation: alert lookup, lineage tracing, ETL checks, query analysis, and data profiling. Activates when a user asks about data issues, incidents, alerts, or why data looks wrong.
日本語の概要は準備中です。原文の説明を表示しています。
Check the health of a data table/asset using Monte Carlo. Activates on "how is table X", "check health of X", "is X healthy", "status of X", "check on X table", or any health/status question about a data asset.
日本語の概要は準備中です。原文の説明を表示しています。
Route data-related requests to the right Monte Carlo skill or workflow. USE WHEN alerts, incidents, data broken, stale, coverage gaps, data quality, or any ambiguous data observability request.
日本語の概要は準備中です。原文の説明を表示しています。