Webinar: Connecting Agents to Your Data With EnrichMCP

​Agents are only as useful as the data they can access. EnrichMCP transforms your existing data models, such as SQLAlchemy schemas, into an agent-ready MCP server. It exposes type-checked, callable methods that agents can discover, reason about, and invoke directly.

In this upcoming webinar, we’ll connect EnrichMCP to a live database, run real agent queries, and walk through how it builds a semantic interface over your data. We’ll cover relationship navigation (like user to orders to products), how input and output are validated with Pydantic, and how to extend the server with custom logic or non-SQL sources. Finally, we’ll discuss performance, security, and how to bring this pattern into production.

​Format:

​A 40-minute live demo followed by Q&A. All code shown will be shared.

​See you then!

What is EnrichMCP
EnrichMCP is a Python framework that helps AI agents understand and navigate your data. Built on MCP (Model Context Protocol), it adds a semantic layer that turns your data model into typed, discoverable tools, like an ORM for AI.

Think of it as SQLAlchemy for AI agents. EnrichMCP automatically:

• ​Generates typed tools from your data models

• ​Handles relationships between entities (users → orders → products)

• ​Provides schema discovery so AI agents understand your data structure

• ​Validates all inputs/outputs with Pydantic models

• ​Works with any backend - databases, APIs, or custom logic

​Start using EnrichMCP here: https://github.com/featureform/enrichmcp