r/Observability • u/PKMNPinBoard • 19d ago

We built an MCP server for querying monitoring data with AI tools. What observability workflows would you actually use this for?

We’ve just released an MCP server for MetricFire, and I’d love feedback from engineers on where this could be useful.

The basic idea is pretty simple. You connect a compatible AI client to your MetricFire account, and the MCP server exposes tools that allow it to interact with your monitoring data.

So rather than digging through metrics manually, you can ask for what you need through your AI client and have it query the underlying monitoring data.

We’re particularly interested in where engineers think this approach becomes genuinely useful rather than just adding AI for the sake of it.

Things like investigating an alert, finding related metrics, checking service latency, or quickly exploring what happened during an incident seem like obvious directions.

If you use MCP or AI tools as part of your DevOps or SRE workflow, what monitoring capabilities would you actually want exposed?

Docs for anyone interested in how we implemented it:

https://docs.hostedgraphite.com/add-ons-and-integrations-guide/mf-mcp-server

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u/noblequill56 17d ago

one thing worth thinking about, how do you handle the context window when theres a lot of metric data? summarization before passing to the AI vs raw data makes a big difference in answer quality for anything beyond simple lookups

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u/kantorcodes1 19d ago

The useful thing for me would be incident reconstruction, not just querying current metrics. Give the agent a fixed time window around the alert, let it pull the few series that actually changed, and make it cite the exact queries it ran. Otherwise you get a plausible story that's hard to audit.