grafana-tst

name	grafana-tst
description	Tools for interaction with grafana testing environment

Grafana Test Environment Skill

Metadata

• Skill Name: grafana-tst
• Trigger Tag: #grafana-tst
• MCP Server: Grafana MCP Server (Test)
• Category: Monitoring (Test Environment)

Description

Access Grafana test/staging environment for observability data. Covers metrics (Prometheus), logs (Loki), traces (Tempo), profiling (Pyroscope), incident management, OnCall schedules, Sift investigations, alerting, and dashboard management. Use for development, testing, and staging workflows.

Capabilities

Metrics (Prometheus)

• Query PromQL against test Prometheus datasources
• List metric names, label names, label values, and metadata
• Query histograms and percentiles

Logs (Loki)

• Execute LogQL queries for test log retrieval
• List log label names and values
• Query log patterns and statistics
• Stream log data with filtering and parsing

Traces (Tempo)

• Find slow requests in test environment
• Analyze distributed traces

Profiling (Pyroscope)

• Fetch CPU, memory, goroutine profiles for test services
• List profile types and label values per service

Incidents & OnCall

• Create, list, and get incident details
• Add timeline notes to incidents
• List OnCall schedules, teams, users
• Get current on-call users for a schedule

Sift Investigations

• Create and retrieve automated investigations
• Find error patterns in logs
• Find slow requests across services

Alerting

• List, get, create, update, delete alert rules
• List contact points and notification policies
• List alert groups from OnCall

Dashboards

• Search, get, create, update dashboards
• Get panel queries and dashboard summaries
• Generate deeplinks to dashboards or panels
• Render panel/dashboard images (PNG)
• Create and manage annotations

Datasources

• List, get datasources by name or UID

Activation

Include #grafana-tst tag in your prompt to activate this skill.

Usage Examples

Test Metrics

#grafana-tst Show CPU usage for test-api service in staging

Log Investigation

#grafana-tst Search Loki logs for errors in auth service last 1 hour

Dashboard Editing

#grafana-tst Add a new panel to the staging overview dashboard

Alert Testing

#grafana-tst Create a test alert rule for high memory usage

Trace Analysis

#grafana-tst Find slow requests for checkout service in test environment

Configuration

Do NOT search the filesystem for mcp-config.json or similar files directly. Do NOT read ~/.copilot/mcp-config.json directly — always route through custom agent file. MCP server is configured in the grafana-tst.agent.md custom agent file.

Environment Variables

Use environment variables defined in .copilot/.env.

Connectivity Check

Before taking any action, verify the Grafana Test MCP server is reachable:

1. Call list_datasources (with limit=1) as a lightweight probe.
2. If the call fails or returns an error, immediately stop and report: "Grafana test MCP server is unavailable. Cannot proceed."
3. Only proceed with the user's request after a successful probe response.

Best Practices

• Use list_datasources first to find correct datasource UIDs before querying
• Use list_prometheus_metric_names before writing PromQL
• Use list_loki_label_names / list_loki_label_values before writing LogQL
• Use query_loki_stats to check log volume before fetching entries
• Use get_dashboard_summary instead of get_dashboard_by_uid for large dashboards
• Never mix with #grafana-prd in the same request

Limitations

• Test environment only — data does not reflect production
• Different data retention policies than production
• Write operations (create/update alerts, incidents) require appropriate permissions
• Image rendering requires Grafana Image Renderer service

Environment Isolation

CRITICAL: Never use #grafana-tst and #grafana-prd in the same request. Choose one environment per query.

GitHub Copilot & LLM Optimization Context

• Environment Indicator: You are operating within the GitHub Copilot CLI context. Always leverage native GitHub Copilot capabilities when interacting with codebases.
• Model Optimization: This prompt is optimized specifically for Claude Opus 4.5.
  • Leverage Claude Opus 4.5's deep comprehension and superior coding accuracy for complex architectural and logical tasks.
  • Ensure responses are direct, code-focused, and minimize conversational filler to optimize for developer workflows.