Exploring MCP-based tools for Incubator insights

Hi,

I have been experimenting with a small set of tools to explore how we might better understand Incubator data and podling signals in a more queryable way.

The approach is based on simple MCP (Model Context Protocol) servers that expose different layers of Incubator information:
- PodlingsMCP: lifecycle and metadata from podlings.xml
- HealthMCP: parsed metrics from the incubator health reports
- IncubatorMCP: combines the above to produce IPMC-oriented views such as watchlists, readiness signals, and podling briefs

There is also a small skills file included to make it easier for MCP clients to use these tools.

The intent is not to replace existing processes or judgment, but to make it easier to:
-  identify podlings that may need attention
- spot patterns across reports and activity
- generate quick summaries for discussion
- experiment with what the data suggests before applying human context

The higher-level layer (IncubatorMCP) keeps a clear separation between:
-  source facts (from podlings.xml and health reports)
- derived signals (heuristics around activity, mentoring, reporting, and so on)

This is still early and intentionally lightweight, but it may be useful as:
- a way to explore Incubator data programmatically
- a potential aid for reporting or mentoring workflows
- a foundation for further experimentation

Repositories are here if anyone is interested:
-  https://github.com/justinmclean/PodlingsMCP
- https://github.com/justinmclean/HealthMCP
- https://github.com/justinmclean/IncubatorMCP

I would be interested in feedback on a few things:
- whether this kind of tooling would be useful for IPMC work
- whether the signals approach aligns with how people think about podling health
- any obvious gaps or misinterpretations in the heuristics

Note that it can also produce many dashboard-style displays and graphs of the data it has access to.

Thanks,
Justin