[gagege]

In my experience, this kind of thing is exactly what LLMs are good at, and fast at.

Here's a real example from my job as a BI dev. I needed to figure out how to get counts of incoming products from an ERP with a 1000+ table database schema in a data lake with 0 actual foreign keys. I sorta knew the data would need to come from the large set of "stock movements" tables, which I didn't know how to join, and I had no idea which rows from that table would be relevant to incoming product or even which fields to look at to even begin to determine that. I simultaneously asked a consultant for the ERP how to do it and asked Cursor a very basic "add the count of incoming units to this query" request.

Cursor gave me a plausible answer instantly, but I wasn't sure it was correct. When the consultant got back to be a few days later, the answer he gave was identical to Cursor's code. Cursor even thought of an edge case that the consultant hadn't.

It blew my mind! I don't know if Cursor just knows about this ERP's code or what, or if it ran enough research queries to figure it out. But it got it right. The only context I provided was the query I wanted to add the count to and the name of the ERP.

So, I 100% believe that, especially with something like MCP, the pull model is the right way. Let the LLM do the hard work of finding all the context.