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:

bash

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:

bash

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-cosmosdb-mongo-ru/config.md 2>/dev/null || echo "NOT_CONFIGURED"
• Prior report: ! [ -f memory/amg-check-cosmosdb-mongo-ru/report.md ] && echo "exists ($(grep -c '^### BUG-' memory/amg-check-cosmosdb-mongo-ru/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-cosmosdb-mongo-ru/report.md.

Cosmos DB for MongoDB (RU) 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).
• Safe interval: Always use PT1H — it works for all Cosmos DB metrics. PT6H is NOT supported. DataUsage, IndexUsage, and DocumentCount do NOT support P1D.
• 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: Accounts discovered (N=?)
• Phase 1c: Non-succeeded accounts investigated (if any)
• Phase 2: Metric definitions validated
• Phase 3: Pulse check completed (N scanned, N findings)
• Phase 4: Deep metrics for abnormal accounts
• Phase 5: Resource logs for abnormal accounts
• Report presented
• Known issues updated in memory/amg-check-cosmosdb-mongo-ru/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.documentdb/databaseaccounts (lowercase) with kind == 'MongoDB'

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-cosmosdb-mongo-ru/config.md, warn user, use new UID.
• Not found → abort with error.

Phase 1b: Discover All Cosmos DB for MongoDB (RU) Accounts#

text

azureMonitorDatasourceUid: {DATASOURCE_UID}
query: |
  resources
  | where type == 'microsoft.documentdb/databaseaccounts'
  | where kind == 'MongoDB'
  | project name, resourceGroup, location, subscriptionId, id, properties.provisioningState
  | order by location asc, name asc

If the config specifies subscription IDs (not "all"), add | where subscriptionId in ('{ID1}', '{ID2}'). Derive region summary by counting accounts per location. Flag accounts not in "Succeeded" state. Stop if zero accounts found.

Why kind == 'MongoDB'? Filters for RU-based MongoDB API accounts. vCore-based MongoDB uses microsoft.documentdb/mongoclusters.

Phase 1c: Activity Log for Non-Succeeded Accounts#

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

text

azureMonitorDatasourceUid: {DATASOURCE_UID}
scope: {account's full ARM resource ID}
startTime: now-3d
endTime: now
select: eventTimestamp,operationName,status,caller,subStatus

If the response exceeds 500 KB, retry with startTime: now-1d. Summarize: operations performed, caller type, success/in-progress status, likely cause.

Phase 2: Validate Available Metrics#

Call amgmcp_query_resource_metric_definition on the first account from Phase 1. Confirm expected metrics exist. Run only once — definitions are the same across all accounts.

Phase 3: Tier 1 — Fleet-Wide Pulse Check#

text

azureMonitorDatasourceUid: {DATASOURCE_UID}
pastDays: 7
scenarios: cosmosdb_mongo

Scans all accounts across 3 scenarios: cosmosdb_mongo_ru, cosmosdb_mongo_throttling, cosmosdb_mongo_availability.

Before moving to Phase 4, verify:

1. scanSummary.totalResourcesScanned matches Phase 1 account count.
2. All 3 scenarios show status: "completed" in scenarioResults.
3. If errors non-empty, retry affected scenarios individually.
4. If >10% accounts missing, fall back to batched amgmcp_query_resource_metric for unscanned accounts.

Accounts in the findings array are abnormal. Also flag any non-Succeeded accounts from Phase 1.

Note: Sustained-high detection (>50% for 6+ hours), RU spike pattern detection (>30pp jump in 1h), and latency analysis require hourly time-series data and are performed in Phase 4 on flagged accounts only.

Phase 4: Tier 2 — Deep Metrics for Abnormal Accounts#

Read reference/phase4-deep-metrics.md before starting Phase 4. It contains:

• Response size management (critical — fleet-wide PT1H queries exceed 500 KB)
• Fleet-wide triage strategy (when >50% accounts are flagged)
• Core and secondary metrics tables
• Batch strategy and correlation analysis patterns (use ultrathink)

Phase 5: Resource Logs for Abnormal Accounts#

Read reference/phase5-resource-logs.md before starting Phase 5. It contains:

• 5 KQL query templates: throttling, high latency, request volume, top RU operations, error codes
• Fallback table guidance (CDBDataPlaneRequests if CDBMongoRequests is empty)

---

Output#

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

Classification:

SeverityCriteriaCRITICALNormalizedRU = 100% sustained, OR ServiceAvailability < 99.9%, OR latency avg > 50msHIGHNormalizedRU max 85-100% with frequent spikes, OR ReplicationLatency > 1000msWARNINGNormalizedRU max 70-85% sustained, OR sustained RU > 50% for 6h+, OR RU spike >30pp in 1h, OR ServiceAvailability < 99.99%, OR latency avg > 10ms, OR ReplicationLatency > 100msMODERATENormalizedRU max 50-70%HEALTHYAll metrics within normal ranges (NormalizedRU < 50%)

Update Known Issues#

After presenting findings, update memory/amg-check-cosmosdb-mongo-ru/report.md:

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

Only add genuine issues: sustained throttling, availability drops, high latency patterns, or replication problems. 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

Reference#

• Cosmos DB resource type: microsoft.documentdb/databaseaccounts (kind: MongoDB)
• vCore resource type (different): microsoft.documentdb/mongoclusters
• Latency metrics: ServerSideLatencyDirect and ServerSideLatencyGateway (the old ServerSideLatency is deprecated)
• Resource log tables: CDBMongoRequests (primary), CDBDataPlaneRequests (fallback)
• Key error codes: 429 / 16500 (throttling), 50 (server error), 13 (unauthorized)
• Safe metric interval: PT1H for all metrics (PT6H NOT supported)
• Known issues: memory/amg-check-cosmosdb-mongo-ru/report.md
• User config: memory/amg-check-cosmosdb-mongo-ru/config.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 Cosmos DB for MongoDB (RU) accounts, then present the results as a table and ask the user which subscription(s) to use:

text

resources
| where type == 'microsoft.documentdb/databaseaccounts'
| where kind == 'MongoDB'
| join kind=inner (
    resourcecontainers
    | where type == 'microsoft.resources/subscriptions'
    | project subscriptionId, subscriptionName=name
) on subscriptionId
| summarize AccountCount=count() by subscriptionId, subscriptionName
| order by AccountCount desc

Present the results as a table with columns: Subscription Name, Subscription ID, Account Count. Then ask the user: "Which subscription ID(s) should I configure for this health check? Or type 'all' to scan all subscriptions."

3. Write config: Write memory/amg-check-cosmosdb-mongo-ru/config.md:

markdown

# amg-check-cosmosdb-mongo-ru Configuration

User-specific values for the Cosmos DB for MongoDB (RU) 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_or_"all"}

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