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EnrichMCP Application

Overview

The EnrichMCP class is the main entry point for creating an enrichmcp application. It manages:

  • Entity registration
  • Resource endpoints
  • Relationship resolution
  • Schema introspection

Class Reference

EnrichMCP(title: str, description: str)

Creates a new enrichmcp application.

Parameters: - title: The name of your API - description: A description of what your API provides

Example: 

from enrichmcp import EnrichMCP

app = EnrichMCP(title="My API", description="API for AI agents to access my data")

Methods

entity(cls=None, *, description=None)

Decorator to register a Pydantic model as an entity.

Parameters: - cls: The model class (when used without parentheses) - description: Optional description override (uses docstring by default)

Example: 

@app.entity
class User(EnrichModel):
    """User in the system."""

    id: int = Field(description="User ID")
    name: str = Field(description="Username")

retrieve(func=None, *, name=None, description=None)

Register a function as an MCP resource. app.resource is deprecated and forwards to this method.

Parameters: - func: The function (when used without parentheses) - name: Optional name override (uses function name by default) - description: Optional description override (uses docstring by default)

Example: 

@app.retrieve
async def list_users() -> list[User]:
    """List all users in the system."""
    return await fetch_all_users()

create(func=None, *, name=None, description=None)

Register an entity creation operation. Works like @app.retrieve but indicates a create action.

@app.create
async def create_user(email: str) -> User:
    ...

update(func=None, *, name=None, description=None)

Register an entity update using a patch model containing mutable fields.

@app.update
async def update_user(uid: int, patch: User.PatchModel) -> User:
    ...

delete(func=None, *, name=None, description=None)

Register an entity deletion operation.

@app.delete
async def delete_user(uid: int) -> bool:
    ...

get_context() -> EnrichContext

Return the current request context as an :class:~enrichmcp.EnrichContext.

app = EnrichMCP("My API", description="desc")
ctx = app.get_context()
assert ctx.fastmcp is app.mcp

run(**options)

Start the MCP server.

Parameters: - transport: Transport protocol - "stdio", "sse", or "streamable-http". - mount_path: Optional mount path for SSE transport. - **options: Additional options forwarded to FastMCP.

Example: 

if __name__ == "__main__":
    app.run()  # stdio default
    app.run(transport="streamable-http")

describe_model() -> str

Generate a comprehensive description of the data model. This is used internally by the explore_data_model() resource.

Returns: A formatted string containing all entities, fields, and relationships. If a field is annotated with typing.Literal, the allowed values are shown in the output.

Built-in Resources

explore_data_model()

Every EnrichMCP application automatically provides this resource for AI agents to discover the data model.

Example Response: 

{
    "title": "My API",
    "description": "API for AI agents to access my data",
    "entity_count": 3,
    "entities": ["User", "Order", "Product"],
    "model": "# Data Model: My API\n...",
    "usage_hint": "Use the model information above..."
}

Entity Registration Flow

  1. Validation: Checks that entity has description and all fields have descriptions
  2. Registration: Adds entity to the app's registry
  3. Relationship Setup: Configures relationship fields for resolution

Resource Registration Flow

  1. Validation: Ensures resource has a description
  2. Wrapping: Wraps function with MCP tool decorator
  3. Registration: Makes resource available to AI agents

Error Handling

The app validates all relationships have resolvers before starting:

# This will raise ValueError on app.run()
@app.entity
class User(EnrichModel):
    orders: list[Order] = Relationship(description="User's orders")
    # Missing: @User.orders.resolver


app.run()  # ValueError: User.orders missing resolver

Best Practices

  1. Create one app instance - Share it across your modules
  2. Register entities first - Before defining resolvers
  3. Use descriptive names - Help AI agents understand your API
  4. Validate early - The app catches configuration errors at startup

Complete Example

from enrichmcp import EnrichMCP, EnrichModel, Relationship
from pydantic import Field

# Create app
app = EnrichMCP(title="Bookstore API", description="API for managing books and authors")


# Define entities
@app.entity
class Author(EnrichModel):
    """Book author."""

    id: int = Field(description="Author ID")
    name: str = Field(description="Author name")

    books: list["Book"] = Relationship(description="Books by this author")


@app.entity
class Book(EnrichModel):
    """Book in the catalog."""

    id: int = Field(description="Book ID")
    title: str = Field(description="Book title")
    author_id: int = Field(description="Author ID")

    author: Author = Relationship(description="Book author")


# Define resolvers
@Author.books.resolver
async def get_author_books(author_id: int) -> list[Book]:
    """Get all books by an author."""
    # Implementation here
    return []


@Book.author.resolver
async def get_book_author(book_id: int) -> Author:
    """Get the author of a book."""
    # Implementation here
    return Author(id=1, name="Unknown")


# Define resources
@app.retrieve
async def list_authors() -> list[Author]:
    """List all authors."""
    # Implementation here
    return []


# Run
if __name__ == "__main__":
    app.run()