enrichmcp

That's an odd question. If you have a regular ORM how do you handle sensitive data that your user shouldn't know about? You add some logic or filters so that the user can only query their own data, or other data they have permission to access. It's also addressed directly in the README. https://github.com/featureform/enrichmcp?tab=readme-ov-file#... I know LLMs can be scary, but this is the same problem that any ORM or program that handles user data would deal with.

I've been working with the Featureform team on their new open-source project, [EnrichMCP][1], a Python ORM framework that helps AI agents understand and interact with your data in a structured, semantic way. EnrichMCP is built on top of [MCP][2] and acts like an ORM, but for agents instead of humans. You define your data model using SQLAlchemy, APIs, or custom logic, and EnrichMCP turns it into a type-safe, introspectable interface that agents can discover, traverse, and invoke. It auto-generates tools from your models, validates all I/O with Pydantic, handles relationships, and supports schema discovery. Agents can go from user → orders → product naturally, just like a developer navigating an ORM. We use this internally to let agents query production systems, call APIs, apply business logic, and even integrate ML models. It works out of