| name | azure_managed_grafana |
| description | Expertise in Azure Managed Grafana including dashboards, Azure Monitor metrics, Application Insights failures and traces, Azure Data Explorer (Kusto) queries, SQL Server queries, and Azure Resource Graph. Use for querying observability data, searching dashboards, investigating failures, analyzing GenAI agent telemetry, and running KQL or SQL queries via the AMG-MCP endpoint. |
Azure Managed Grafana Observability Skill
You have access to the Azure Managed Grafana MCP server (AMG-MCP) tools for querying observability data, dashboards, and connected data sources.
Available Tools
| Tool | Purpose |
|---|
| amgmcp_insights_get_failures | Get failure insights from Application Insights — failed requests, dependencies, and exceptions |
| amgmcp_insights_get_agents | Get GenAI agent insights from Application Insights — invocations, token usage, latency (OpenTelemetry GenAI Semantic Conventions) |
| amgmcp_kusto_get_metadata | List connected Azure Data Explorer clusters with databases and schema |
| amgmcp_kusto_query | Execute KQL queries against Azure Data Explorer clusters |
| amgmcp_mssql_get_metadata | List connected SQL Server data sources with databases, tables, and column schemas |
| amgmcp_mssql_query | Execute SQL queries against connected Microsoft SQL Server data sources |
| amgmcp_query_application_insights_trace | Query Application Insights trace data, aggregating across multiple instances |
| amgmcp_query_azure_subscriptions | List Azure subscriptions accessible through the Grafana Azure Monitor data source |
| amgmcp_query_resource_graph | Execute Azure Resource Graph (ARG) queries for resource inventory and configuration |
| amgmcp_query_resource_log | Query Azure resource logs through the Azure Monitor data source |
| amgmcp_query_resource_metric | Query Azure resource metric values through the Azure Monitor data source |
| amgmcp_query_resource_metric_definition | Get available metric definitions for Azure resources |
| amgmcp_dashboard_search | Search Grafana dashboards by query string — returns title, UID, folder, tags, and URL |
| amgmcp_datasource_list | List all configured Grafana data sources |
Workflow
General Investigation
- Understand the scope — Identify which Azure resources, services, or data sources are involved
- Discover data sources — Use
amgmcp_datasource_list to see what's connected
- Check subscriptions — Use
amgmcp_query_azure_subscriptions to list accessible subscriptions
- Query relevant data — Use the appropriate tool for the data type (metrics, logs, traces, KQL, SQL)
- Search dashboards — Use
amgmcp_dashboard_search to find existing dashboards with relevant visualizations
- Analyze and recommend — Summarize findings with actionable next steps
Failure Investigation
- Get failure overview — Use
amgmcp_insights_get_failures to retrieve failure summary (failed requests, dependencies, exceptions)
- Trace failures — Use
amgmcp_query_application_insights_trace to follow request traces across services
- Check metrics — Use
amgmcp_query_resource_metric to correlate with resource-level metrics (CPU, memory, latency)
- Query logs — Use
amgmcp_query_resource_log for detailed log entries around the failure time
- Check resource health — Use
amgmcp_query_resource_graph to verify resource configuration and status
Azure Monitor Metrics Analysis
- Discover metrics — Use
amgmcp_query_resource_metric_definition to list available metrics for a resource
- Query metric values — Use
amgmcp_query_resource_metric with the desired metric name, aggregation, and time range
- Correlate — Check related resources' metrics to identify cascading issues
Azure Data Explorer (Kusto) Queries
- Get cluster metadata — Use
amgmcp_kusto_get_metadata to list clusters, databases, and table schemas
- Write and run KQL — Use
amgmcp_kusto_query to execute Kusto Query Language queries
Common KQL patterns:
// Recent errors from a table
TableName
| where Timestamp > ago(1h)
| where Level == "Error"
| project Timestamp, Message, Source
| order by Timestamp desc
| take 100
// Aggregated error counts
TableName
| where Timestamp > ago(24h)
| summarize ErrorCount = count() by bin(Timestamp, 1h), Source
| order by Timestamp desc
// Percentile latency analysis
RequestTable
| where Timestamp > ago(1h)
| summarize p50 = percentile(Duration, 50),
p95 = percentile(Duration, 95),
p99 = percentile(Duration, 99)
by bin(Timestamp, 5m)
SQL Server Queries
- Get schema metadata — Use
amgmcp_mssql_get_metadata to list databases, tables, and columns
- Run queries — Use
amgmcp_mssql_query to execute SQL queries
GenAI Agent Telemetry
Use amgmcp_insights_get_agents to analyze GenAI agent performance. This tool queries data following OpenTelemetry Generative AI Semantic Conventions and returns:
- Agent invocation counts
- Token usage (input/output)
- Latency distributions
- Error rates
Azure Resource Graph Queries
Use amgmcp_query_resource_graph to query resource inventory across subscriptions:
// List all VMs and their status
Resources
| where type == "microsoft.compute/virtualmachines"
| project name, location, properties.hardwareProfile.vmSize, powerState = properties.extended.instanceView.powerState.code
// Find resources by tag
Resources
| where tags["environment"] == "production"
| summarize count() by type
// Check NSG rules
Resources
| where type == "microsoft.network/networksecuritygroups"
| mv-expand rules = properties.securityRules
| project name, ruleName = rules.name, access = rules.properties.access, direction = rules.properties.direction
Best Practices
- Start with data source discovery — Always use
amgmcp_datasource_list to understand what's connected before querying
- Check metric definitions first — Use
amgmcp_query_resource_metric_definition before querying metrics to get correct names
- Get schema before querying — Use
amgmcp_kusto_get_metadata or amgmcp_mssql_get_metadata to discover table/column names
- Use dashboards for context — Search for existing dashboards with
amgmcp_dashboard_search — they often reveal which metrics and queries are most relevant
- Scope time ranges — Always specify reasonable time ranges to avoid excessive data retrieval
- Aggregate across instances — Use
amgmcp_query_application_insights_trace when traces span multiple Application Insights instances
Troubleshooting
| Issue | Solution |
|---|
| 401/403 errors | Verify the service account token or Entra ID token is valid and not expired |
| Token expired | Grafana service account tokens don't expire; if using Entra ID, refresh the token |
| No data returned | Verify the Grafana instance has the expected data sources configured |
| Data source not found | Use amgmcp_datasource_list to check available data sources |
| Metric not found | Use amgmcp_query_resource_metric_definition to discover valid metric names |
| KQL query error | Verify table and column names with amgmcp_kusto_get_metadata |
| SQL query error | Verify schema with amgmcp_mssql_get_metadata |
| Dashboard not found | Try broader search terms; search matches title, tags, and folder names |
| Sovereign cloud | AMG-MCP is only available in Azure Public Cloud — sovereign clouds are not yet supported |
