grafana-prd

name	grafana-prd
description	Tools for interaction with grafana production environment

Grafana Production Environment Skill

Metadata

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

Description

Access Grafana production environment for live observability data. Covers metrics (Prometheus), logs (Loki), traces (Tempo), profiling (Pyroscope), incident management, OnCall schedules, Sift investigations, alerting, and dashboard management.

Capabilities

Metrics (Prometheus)

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

Logs (Loki)

• Execute LogQL queries for production 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 production
• Analyze distributed traces

Profiling (Pyroscope)

• Fetch CPU, memory, goroutine profiles for production 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-prd tag in your prompt to activate this skill.

Usage Examples

Live Metrics

#grafana-prd Show current CPU usage for production API service

Log Investigation

#grafana-prd Search production Loki logs for errors in the auth service last 30 minutes

Incident Response

#grafana-prd #fetch Create incident: "Payment service elevated error rate" severity critical

Trace Analysis

#grafana-prd Find slow requests in production for service checkout last 1 hour

OnCall

#grafana-prd Who is currently on call?

Alert Rules

#grafana-prd List all firing alert rules in production

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-prd.agent.md custom agent file.

Environment Variables

Use environment variables defined in .copilot/.env.

Connectivity Check

Before taking any action, verify the Grafana Production 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 production 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-tst in the same request

Limitations

• Production environment — use responsibly; avoid heavy queries during peak hours
• Write operations (create/update alerts, incidents) require appropriate permissions
• Image rendering requires Grafana Image Renderer service
• Rate limits may apply on high-cardinality metric queries

Environment Isolation

CRITICAL: Never use #grafana-prd and #grafana-tst 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.