You could also build an EnrichMCP server that calls your Django server manually

Obviously, it can (and sometimes will) hallucinate and make up why its using a tool. The thing is, we don't really have true LLM explainability so this is the best we can really do.

We also generate a few tools for the LLM specifically to explain the data model to it. It works quite well, even on complex schemas.

The use case is more transactional than analytical, though we've seen it used for both.

I recommend running the openai_chat_agent in examples/ (also supports ollama for local run) and connect it to the shop_api server and ask it a question like : "Find and explain fraud transactions"

I guess you also need per user contexts, such that you depend on the user auth to access user data, and the agent can only access that data.

But this same concern exists for employees in big corps. If I work at google, I probably am not able to access arbitrary data, so I can't leak it.

If you use the lower-level enrichMCP API (without SQLAlchemy) you can fully control all retrieval logic and add things like rate limiting, not dissimilar to how you'd solve this problem with a traditional API.

Time-to-solution and quality would be my guess. In my experience, adding high level important details about the way information is organized to the beginning of the context and then explaining the tools to further explore schema or access data produces much more consistent results rather than each inference having to query the system and build its own world view before trying to figure out how to answer your query and then doing it.

It's a bit like giving you a book or giving you that book without the table of contents and no index, but you you can do basic text search over the whole thing.