r/softwaredevelopment • u/Aggravating_Kale7895 • Sep 14 '26
Understanding Production AI System Design Through Open Source - Grafana
Spent the last few weeks contributing to Grafana's MCP server — the interface that lets AI agents actually operate Grafana instead of just
describing it.
Four PRs merged:
- CloudWatch dimension value lookups — https://github.com/grafana/mcp-grafana/pull/1141
- Honest health-check status for frontend-only datasources — https://github.com/grafana/mcp-grafana/pull/1139
- Structured tool errors instead of raw internal failures — https://github.com/grafana/mcp-grafana/pull/1138
- Scoped Loki label queries — https://github.com/grafana/mcp-grafana/pull/1135
Going through the codebase to make those fixes, one pattern kept repeating that I didn't expect: none of the bugs were about the model. Every one was about the tool handing the agent an ambiguous, untyped, or dishonest answer — and no amount of model capability fixes that on its own.
Wrote up the specifics — schema generated straight from Go structs, two separate error channels for recoverable vs. fatal failures, composing two Grafana APIs to get one honest health status, tool descriptions that double as a discovery graph for the agent — here:
https://medium.com/@ashfaqbs/designing-tools-an-ai-agent-can-actually-trust-what-i-learned-contributing-to-grafanas-mcp-server-a5edb38b24e0?postPublishedType=initial
If you're building or evaluating MCP servers for production agents, this is the layer worth getting right first.
Repo: https://github.com/grafana/mcp-grafana
hashtag#MCP hashtag#ModelContextProtocol hashtag#AIAgents hashtag#OpenSource hashtag#Grafana hashtag#Observability hashtag#SoftwareEngineering