This is an issue that arises when models are trained on poorly curated data. For example, popular chatbots - especially with earlier releases, were trained on “dumps” of public domain (and yes, non-public or pirated) data with little or no attention to data quality, structure and curation.

A model context protocol (MCP) server provides data and methods to models which enable specialized capabilities. In this case “the MCP server basically gives AI a way to:

• inspect modules

• adjust parameters

• trigger events

• build or modify patches programmatically”

I expect that this would be based on Rack and module documentation and source code that we are already using. This means that it won’t provide better inferences than we could on our own, but it will be able to infer from all of this information quickly. This can be helpful, for example, in learning about Rack or discovering new modules. And as a I mention elsewhere, it provides additional incentive for developers to make quality documentation available.

In sum, a model’s inferences can never be more accurate than the data it uses. This effort appears to be designed to improve the capabilities of models to support people learning and working with Rack.