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EnrichMCP is a python framework for building data driven MCP servers
| Date | Stars |
|---|---|
| 2026-07-31 | 646 |
| 2026-08-06 | 644 |
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# EnrichMCP
**The ORM for AI Agents - Turn your data model into a semantic MCP layer**
[](https://github.com/featureform/enrichmcp/actions/workflows/ci.yml)
[](https://codecov.io/gh/featureform/enrichmcp)
[](https://pypi.org/project/enrichmcp/)
[](https://www.python.org/downloads/)
[](https://github.com/featureform/enrichmcp/blob/main/LICENSE)
[](https://featureform.github.io/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.
## What is EnrichMCP?
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
## Installation
```bash
pip install enrichmcp
# With SQLAlchemy support
pip install enrichmcp[sqlalchemy]
```
## Show Me Code
### Option 1: I Have SQLAlchemy Models (30 seconds)
Transform your existing SQLAlchemy models into an AI-navigable API:
```python
from enrichmcp import EnrichMCP
from enrichmcp.sqlalchemy import (
include_sqlalchemy_models,
sqlalchemy_lifespan,
EnrichSQLAlchemyMixin,
)
from sqlalchemy import ForeignKey
from sqlalchemy.ext.asyncio import create_async_engine
from sqlalchemy.orm import DeclarativeBase, Mapped, mapped_column, relationship
engine = create_async_engine("postgresql+asyncpg://user:pass@localhost/db")
# Add the mixin to your declarative base
class Base(DeclarativeBase, EnrichSQLAlchemyMixin):
pass
class User(Base):
"""User account."""
__tablename__ = "users"
id: Mapped[int] = mapped_column(primary_key=True, info={"description": "Unique user ID"})
email: Mapped[str] = mapped_column(unique=True, info={"description": "Email address"})
status: Mapped[str] = mapped_column(default="active", info={"description": "Account status"})
orders: Mapped[list["Order"]] = relationship(
back_populates="user", info={"description": "All orders for this user"}
)
class Order(Base):
"""Customer order."""
__tablename__ = "orders"
id: Mapped[int] = mapped_column(primary_key=True, info={"description": "Order ID"})
user_id: Mapped[int] = mapped_column(
ForeignKey("users.id"), info={"description": "Owner user ID"}
)
total: Mapped[float] = mapped_column(info={"description": "Order total"})
user: Mapped[User] = relationship(
back_populates="orders", info={"description": "User who placed the order"}
)
# That's it! Create your MCP app
app = EnrichMCP(
"E-commerce Data",
"API generated from SQLAlchemy models",
lifespan=sqlalchemy_lifespan(Base, engine, cleanup_db_file=True),
)
include_sqlalchemy_models(app, Base)
if __name__ == "__main__":
app.run()
```
AI agents can now:
- `explore_data_model()` - understand your entire schema
- `list_users(status='active')` - query with filters
- `get_user(id=123)` - fetch specific records
- Navigate relationships: `user.orders` → `order.user`
### Option 2: I Have REST APIs (2 minutes)
Wrap your existing APIs with semantic understanding:
```python
from typing import Literal
from enrichmcp import EnrichMCP, EnrichModel, Relationship
from pydantic import Field
import httpx
app = EnrichMCP("API Gateway", "Wrapper around existing REST AExcerpt of 15,374 characters
Read on GitHub
Simba Khadder · Featureform · United States
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Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:db94923413f7fda4, desc:mcp servers