GrafanaToolServer-by-Cumhur

cmakkaya/GrafanaToolServer-by-Cumhur

3.2

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The GrafanaToolServer is a server that wraps Grafana's API endpoints into tool functions for AI agents to use.

GrafanaToolServer (by Cumhur Akkaya)

The GrafanaToolServer works by wrapping Grafana’s API endpoints into tool functions that AI agents (e.g., Claude, OpenAI GPT, or other MCP-compatible models) can invoke.

🎯 Why This Project?

Grafana Tool Server acts as a utility server that exposes Grafana capabilities to AI models like the MCP ecosystem. It allows LLMs to interact with Grafana dashboards, query Prometheus data, trigger alerts, or retrieve metrics - all through secure, permissioned tool calls. The GrafanaToolServer works by wrapping Grafana's API endpoints into tool functions that AI agents (e.g., Claude, OpenAI GPT, or other MCP-compatible models) can invoke. This enables automated observability, diagnosis, and metric analysis directly through conversational AI.

We will use the Grafana Tool Server in the integration of Ollama and LLMs.

🔧 Architecture

┌──────────────────┐
│   Ollama Web UI  │
│  (Ollama + LLMs) │
└────────┬─────────┘
         │ MCP Protocol
         │ 
┌────────▼─────────┐
│ GrafanaToolServer│ Docker +
│  (This Project)  │ Python
└────────┬─────────┘
         │ n8n workflow
         │
┌────────▼────────┐
│ Grafana Server  │
│      API        │
└─────────────────┘

Related Article: Step-by-Step Tutorial: Observability 3.0: AI-Powered APM = Claude (cloud-based) / Ollama (self-hosted) + MCP Server + Observability Stack

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In today’s cloud-based world, observability is no longer just about collecting logs, metrics, and traces. We can take observability to the next level with AI-powered APM (Application Performance Monitoring). Tools like Claude (cloud-based AI) and Ollama (self-hosted AI), combined with Prometheus, Grafana, Loki, Tempo, and OpenTelemetry, enable us to create an intelligent observability ecosystem. In this hands-on guide, I’ll show you step-by-step how to integrate AI with observability platforms. We’ll run with various LLM models and compare their results. The integration of AI and observability systems will enable real-time anomaly detection and faster root cause analysis for your systems. I will share with you a comprehensive overview of my experiences with integrating AI and observability systems.

📗 Medium Articles Link:

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I hope they are useful to you.

🙏 I wish you growing success.

🤝 Contributing

We welcome your contributions! Feel free to send pull requests.

  1. Fork it
  2. Create your feature branch (git checkout -b feature/amazing)
  3. Commit your changes (git commit -m 'feat: Add amazing feature')
  4. Push to the branch (git push origin feature/amazing)
  5. Open a Pull Request

Connect with me 📫 You can learn more about me

📝 License

MIT License - See file for details.

🙏 Acknowledgments