AMG PostgreSQL Flexible Server Health Check

MCP Tools

Run only when the user explicitly asks for a fleet-wide PostgreSQL Flexible Server health check — scans CPU, memory, storage, IOPS, disk bandwidth, and connection metrics across all servers, then deep-dives into abnormal servers with resource logs and correlation analysis. Tracks known issues across sessions via persistent report. Uses AMG-MCP pulse check for Tier 1 triage, then batched Azure Monitor queries for Tier 2 investigation. On first run, auto-discovers datasource UID and prompts for subscription ID.

Install

openclaw skills install amg-check-pg-flex

OpenClaw Setup (one-time)

This skill calls MCP tools prefixed with mcp__amg__*, so OpenClaw must have an MCP server registered under the exact name amg. Run this once per workspace before invoking the skill:

openclaw mcp set amg '{"url":"https://<your-grafana-instance>/api/azure-mcp","transport":"streamable-http","headers":{"Authorization":"Bearer <your-token>"}}'

Replace <your-grafana-instance> with your Azure Managed Grafana endpoint and <your-token> with a valid Grafana service-account token (starts with glsa_). The server name must be amg — the skill's allowed-tools reference mcp__amg__* and will not find tools under any other name.

Verify the server is registered:

openclaw mcp list

Official skill source: https://github.com/Azure/amg-skills

Runtime Context

Current UTC time: !date -u +%Y-%m-%dT%H:%M:%SZ
Config: !cat memory/amg-check-pg-flex/config.md 2>/dev/null || echo "NOT_CONFIGURED"
Prior report: ![ -f memory/amg-check-pg-flex/report.md ] && echo "exists ($(grep -c '^### BUG-' memory/amg-check-pg-flex/report.md) bugs documented)" || echo "not found"
Arguments: time-range=$0, subscription-override=$1

Known Issues: Before presenting findings, cross-reference results against memory/amg-check-pg-flex/report.md.

AMG PostgreSQL Flexible Server Health Check

Critical Constraints

No subagents for MCP. The Agent tool cannot access MCP tools — all MCP calls must be made from the main context.
Scan every resource. No sampling or early stopping.
Time format: ISO 8601 UTC with explicit from/to — NEVER use timespan (it causes errors).
Parallelism cap: 30 concurrent MCP calls per batch. Reduce to 4-5 if rate-limited.
Result too large: Save to temp file and parse outside the context window. Prefer node -e "..." if installed; otherwise fall back to python -c "...", jq, or pwsh -Command "...". Bash permission for the chosen interpreter will be prompted on first use.

Progress Tracking

Update checkboxes as you complete each phase:

Phase 1a: Datasource validated
Phase 1b: Servers discovered (N=?)
Phase 1c: Non-ready servers investigated (if any)
Phase 2: Pulse check completed (N scanned, N findings)
Phase 3: Deep metrics for abnormal servers
Phase 4: Resource logs for abnormal servers
Report presented
Known issues updated in memory/amg-check-pg-flex/report.md

Configuration

If Config shows NOT_CONFIGURED: Run First-Run Setup at the bottom of this file, then return here.

If Config is populated: Extract the datasource UID and subscription ID from the pre-loaded Runtime Context above and use them for all queries. Use $1 as the subscription override if provided.

Datasource UID: from ## Azure Monitor Datasource > UID
Subscription ID: from ## Subscription (or $1 if provided)
Resource Type: microsoft.dbforpostgresql/flexibleservers (lowercase)
ARM ID template: /subscriptions/{SUB}/resourceGroups/{RG}/providers/Microsoft.DBforPostgreSQL/flexibleServers/{name}

Time Range

Default: 7 days for metrics, 24 hours for logs. Override with $0 (e.g., 3d). Keep log queries to 1-2 days to avoid timeouts.

Workflow

Phase 1a: Validate Datasource

Call amgmcp_datasource_list (no parameters). Find entry with type == "grafana-azure-monitor-datasource".

Matches configured UID → proceed.
Different UID → update memory/amg-check-pg-flex/config.md, warn user, use new UID.
Not found → abort with error.

Phase 1b: Discover All PostgreSQL Flexible Servers

azureMonitorDatasourceUid: {DATASOURCE_UID}
query: |
  resources
  | where type == 'microsoft.dbforpostgresql/flexibleservers'
  | where subscriptionId == '{SUBSCRIPTION_ID}'
  | project name, resourceGroup, location, properties.state, sku.name, sku.tier
  | order by location asc, name asc

If multiple subscriptions are configured, query each separately and merge results. Derive region summary by counting servers per location. Flag servers not in "Ready" state. Stop if zero servers found.

Phase 1c: Activity Log for Non-Ready Servers

If any servers are not in "Ready" state, query the activity log for up to 3 of them:

azureMonitorDatasourceUid: {DATASOURCE_UID}
scope: /subscriptions/{SUB}/resourceGroups/{RG}/providers/Microsoft.DBforPostgreSQL/flexibleServers/{name}
startTime: now-3d
endTime: now
select: eventTimestamp,operationName,status,caller,subStatus

If the response exceeds 500 KB, retry with startTime: now-6h. Summarize: operations performed, caller type (service principal vs human), success/in-progress status, likely cause.

Phase 2: Tier 1 — Fleet-Wide Pulse Check

azureMonitorDatasourceUid: {DATASOURCE_UID}
pastDays: 7
scenarios: pg_flex

Scans all servers across 5 scenarios: pg_flex_cpu, pg_flex_memory, pg_flex_storage, pg_flex_disk_iops, pg_flex_disk_bandwidth.

Before moving to Phase 3, verify:

scanSummary.totalResourcesScanned matches Phase 1 server count.
All 5 scenarios show status: "completed" in scenarioResults.
If errors non-empty, retry affected scenarios individually (e.g., scenarios: pg_flex_cpu).
If >10% servers missing, fall back to batched amgmcp_query_resource_metric for unscanned servers.

Severity thresholds (findings array):

Severity	CPU	Memory	Storage	Disk IOPS	Disk BW
Critical	>90%	>90%	>85%	>90%	>90%
Warning	>80%	>80%	>75%	>80%	>80%

Phase 3: Tier 2 — Deep Metrics for Abnormal Servers

Read reference/phase3-deep-dive.md before starting Phase 3. It contains:

Core metrics table (always query) and secondary metrics table (query on anomaly)
Batch strategy: up to 50 comma-separated resource IDs per call, up to 5 parallel metric calls
Correlation analysis patterns (use ultrathink)

Phase 4: Resource Logs for Abnormal Servers

Read reference/phase4-resource-logs.md before starting Phase 4. It contains:

5 KQL query templates: error distribution, top errors, warning messages, log volume, session duration
Time filter rules (always required to avoid timeouts)

Output

Present the report using the structure in reference/output-format.md.

Classification:

Severity	Criteria
CRITICAL	`is_db_alive` sustained 0, OR CPU max >90%, OR Memory max >90%, OR Storage >85%
WARNING	CPU avg >80%, Memory avg >85%, sustained >60% for 6h+, spike >30pp in 1h, Storage >70%, `connections_failed` >0, `deadlocks` >0, disk IOPS/BW >80%, transactionIDs >1B, longest query >300s, replication delay >30s
HEALTHY	All metrics within normal ranges

Update Known Issues

After presenting findings, update memory/amg-check-pg-flex/report.md:

Read the current file.
Rebuild the Resource Inventory table at the end: every server, full ARM ID, region, SKU, state. Group by region, sorted alphabetically.
Update existing bug status from today's telemetry (resolved / improving / worsening / still active).
Add new bugs with: severity, server name, region, metric evidence, log evidence, root cause, recommended action.
Update the "Updated" date header.

Only add genuine issues: sustained high utilization, crash patterns, connection storms, persistent errors. Skip transient single-hour spikes or expected maintenance windows.

Error Handling

See reference/error-handling.md for the full recovery table.

Analysis Guidance

Known patterns, signals, root causes: reference/analysis-patterns.md
Optional deep-dive KQL queries: reference/deep-dive-queries.md

First-Run Setup

Run only when Config shows NOT_CONFIGURED. After completing, return to the Workflow above.

1. Discover Datasource UID: Call amgmcp_datasource_list. Filter type == "grafana-azure-monitor-datasource". Prefer uid == "azure-monitor-oob" if multiple match. Abort if zero match.

2. Discover Subscription ID: Run this Resource Graph query to list all subscriptions with PostgreSQL Flexible Servers, then present the results as a table and ask the user which subscription(s) to use:

resources
| where type == 'microsoft.dbforpostgresql/flexibleservers'
| join kind=inner (
    resourcecontainers
    | where type == 'microsoft.resources/subscriptions'
    | project subscriptionId, subscriptionName=name
) on subscriptionId
| summarize ServerCount=count() by subscriptionId, subscriptionName
| order by ServerCount desc

Present the results as a table with columns: Subscription Name, Subscription ID, Server Count. Then ask the user: "Which subscription ID(s) should I configure for this health check?"

3. Write config: Write memory/amg-check-pg-flex/config.md:

# amg-check-pg-flex Configuration

User-specific values for the PostgreSQL Flexible Server health check skill.
This file is auto-generated on first run and can be edited manually.

## Azure Monitor Datasource
- **UID**: {discovered_uid}
- **Name**: {discovered_name}

## Subscription
- {subscription_id}

4. Confirm: Show the resolved config and ask for confirmation before proceeding.

AMG PostgreSQL Flexible Server Health Check

Install

OpenClaw Setup (one-time)

Runtime Context

AMG PostgreSQL Flexible Server Health Check

Critical Constraints

Progress Tracking

Configuration

Time Range

Workflow

Phase 1a: Validate Datasource

Phase 1b: Discover All PostgreSQL Flexible Servers

Phase 1c: Activity Log for Non-Ready Servers

Phase 2: Tier 1 — Fleet-Wide Pulse Check

Phase 3: Tier 2 — Deep Metrics for Abnormal Servers

Phase 4: Resource Logs for Abnormal Servers

Output

Update Known Issues

Error Handling

Analysis Guidance

First-Run Setup

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