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Analyze and correlate Infrahub data using the MCP server. Use when querying live infrastructure data to answer operational questions, detect drift, correlate node types, investigate service impact, check maintenance windows, or produce ad-hoc reports — without writing pipeline code.
npx skill4agent add opsmill/infrahub-skills infrahub-analyst../infrahub-check-creator/SKILL.md../infrahub-transform-creator/SKILL.md| Priority | Category | Prefix | Description |
|---|---|---|---|
| CRITICAL | MCP Tools | | Available Infrahub MCP tools, invocation patterns, response structure |
| CRITICAL | Query Patterns | | GraphQL structures for fetching, filtering, and traversing relationships |
| HIGH | Correlation | | Joining, diffing, and reasoning over data from multiple queries |
| HIGH | Reporting Output | | Presenting findings: summaries, tables, per-object detail, remediation hints |
| MEDIUM | Approach Selection | | When to use MCP analysis vs InfrahubCheck vs Transform |
mcp__infrahub__infrahub_querymcp__infrahub__infrahub_list_schemamcp__infrahub__infrahub_getmcp__infrahub__infrahub_createmcp__infrahub__infrahub_update# Example: find all devices in an active
# maintenance window
query MaintenanceDevices {
MaintenanceWindow(status__value: "active") {
edges {
node {
name { value }
start_time { value }
end_time { value }
devices {
edges {
node {
name { value }
role { value }
site {
node { name { value } }
}
}
}
}
}
}
}
}1. Understand the question
→ "Which services depend on devices currently
in a maintenance window?"
2. Identify the node types involved
→ MaintenanceWindow, DcimDevice, Service
(or equivalent in your schema)
3. Query current state
→ mcp__infrahub__infrahub_query — one query
per node type, or combined
4. Correlate the data
→ Join across node types, filter, count, diff
5. Report findings
→ Summarize with counts, list affected objects,
suggest next steps