MCPEngine

EnrichMCP

AI Agent 的 ORM——将您的数据模型转化为语义 MCP 层

EnrichMCP 是一个 Python 框架，可帮助 AI Agent 理解和浏览您的数据。它基于 MCP（模型上下文协议）构建，添加了一个语义层，将您的数据模型转化为类型化、可发现的工具——就像面向 AI 的 ORM。

什么是 EnrichMCP？

可以把它看作面向 AI Agent 的 SQLAlchemy。EnrichMCP 会自动：

• 根据您的数据模型生成类型化工具
• 处理实体之间的关系（用户 → 订单 → 商品）
• 提供架构发现功能，让 AI Agent 理解您的数据结构
• 使用 Pydantic 模型验证所有输入和输出
• 支持任何后端——数据库、API 或自定义逻辑

安装

pip install enrichmcp

# With SQLAlchemy support
pip install enrichmcp[sqlalchemy]

看看代码

选项 1：我有 SQLAlchemy 模型（30 秒）

将现有的 SQLAlchemy 模型转换为可供 AI 浏览的 API：

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 Agent 现在可以：

• explore_data_model() - 了解完整架构
• list_users(status='active') - 使用筛选条件查询
• get_user(id=123) - 获取特定记录
• 浏览关系：user.orders → order.user

选项 2：我有 REST API（2 分钟）

为现有 API 添加语义理解能力：

from typing import Literal
from enrichmcp import EnrichMCP, EnrichModel, Relationship
from pydantic import Field
import httpx

app = EnrichMCP("API Gateway", "Wrapper around existing REST APIs")
http = httpx.AsyncClient(base_url="https://api.example.com")


@app.entity()
class Customer(EnrichModel):
    """Customer in our CRM system."""

    id: int = Field(description="Unique customer ID")
    email: str = Field(description="Primary contact email")
    tier: Literal["free", "pro", "enterprise"] = Field(description="Subscription tier")

    # Define navigable relationships
    orders: list["Order"] = Relationship(description="Customer's purchase history")


@app.entity()
class Order(EnrichModel):
    """Customer order from our e-commerce platform."""

    id: int = Field(description="Order ID")
    customer_id: int = Field(description="Associated customer")
    total: float = Field(description="Order total in USD")
    status: Literal["pending", "shipped", "delivered"] = Field(description="Order status")

    customer: Customer = Relationship(description="Customer who placed this order")


# Define how to fetch data
@app.retrieve()
async def get_customer(customer_id: int) -> Customer:
    """Fetch customer from CRM API."""
    response = await http.get(f"/api/customers/{customer_id}")
    return Customer(**response.json())


# Define relationship resolvers
@Customer.orders.resolver
async def get_customer_orders(customer_id: int) -> list[Order]:
    """Fetch orders for a customer."""
    response = await http.get(f"/api/customers/{customer_id}/orders")
    return [Order(**order) for order in response.json()]


@Order.customer.resolver
async def get_order_customer(order_id: int) -> Customer:
    """Fetch the customer for an order."""
    response = await http.get(f"/api/orders/{order_id}/customer")
    return Customer(**response.json())


app.run()

选项 3：我想要完全控制（5 分钟）

使用自定义逻辑构建完整的数据层：

from enrichmcp import EnrichMCP, EnrichModel, Relationship
from datetime import datetime
from decimal import Decimal
from pydantic import Field

app = EnrichMCP("Analytics Platform", "Custom analytics API")

db = ...  # your database connection


@app.entity()
class User(EnrichModel):
    """User with computed analytics fields."""

    id: int = Field(description="User ID")
    email: str = Field(description="Contact email")
    created_at: datetime = Field(description="Registration date")

    # Computed fields
    lifetime_value: Decimal = Field(description="Total revenue from user")
    churn_risk: float = Field(description="ML-predicted churn probability 0-1")

    # Relationships
    orders: list["Order"] = Relationship(description="Purchase history")
    segments: list["Segment"] = Relationship(description="Marketing segments")


@app.entity()
class Segment(EnrichModel):
    """Dynamic user segment for marketing."""

    name: str = Field(description="Segment name")
    criteria: dict = Field(description="Segment criteria")
    users: list[User] = Relationship(description="Users in this segment")


@app.entity()
class Order(EnrichModel):
    """Simplified order record."""

    id: int = Field(description="Order ID")
    user_id: int = Field(description="Owner user ID")
    total: Decimal = Field(description="Order total")


@User.orders.resolver
async def list_user_orders(user_id: int) -> list[Order]:
    """Fetch orders for a user."""
    rows = await db.query(
        "SELECT * FROM orders WHERE user_id = ? ORDER BY id DESC",
        user_id,
    )
    return [Order(**row) for row in rows]


@User.segments.resolver
async def list_user_segments(user_id: int) -> list[Segment]:
    """Fetch segments that include the user."""
    rows = await db.query(
        "SELECT s.* FROM segments s JOIN user_segments us ON s.name = us.segment_name WHERE us.user_id = ?",
        user_id,
    )
    return [Segment(**row) for row in rows]


@Segment.users.resolver
async def list_segment_users(name: str) -> list[User]:
    """List users in a segment."""
    rows = await db.query(
        "SELECT u.* FROM users u JOIN user_segments us ON u.id = us.user_id WHERE us.segment_name = ?",
        name,
    )
    return [User(**row) for row in rows]


# Complex resource with business logic
@app.retrieve()
async def find_high_value_at_risk_users(
    lifetime_value_min: Decimal = 1000, churn_risk_min: float = 0.7, limit: int = 100
) -> list[User]:
    """Find valuable customers likely to churn."""
    users = await db.query(
        """
        SELECT * FROM users
        WHERE lifetime_value >= ? AND churn_risk >= ?
        ORDER BY lifetime_value DESC
        LIMIT ?
        """,
        lifetime_value_min,
        churn_risk_min,
        limit,
    )
    return [User(**u) for u in users]


# Async computed field resolver
@User.lifetime_value.resolver
async def calculate_lifetime_value(user_id: int) -> Decimal:
    """Calculate total revenue from user's orders."""
    total = await db.query_single("SELECT SUM(total) FROM orders WHERE user_id = ?", user_id)
    return Decimal(str(total or 0))


# ML-powered field
@User.churn_risk.resolver
async def predict_churn_risk(user_id: int) -> float:
    """Run churn prediction model."""
    ctx = app.get_context()
    features = await gather_user_features(user_id)
    model = ctx.get("ml_models")["churn"]
    return float(model.predict_proba(features)[0][1])


app.run()

主要功能

🔍 自动架构发现

AI Agent 只需一次调用即可探索您的整个数据模型：

schema = await explore_data_model()
# Returns complete schema with entities, fields, types, and relationships

🔗 关系导航

只需定义一次关系，AI Agent 就能自然地遍历：

# AI can navigate: user → orders → products → categories
user = await get_user(123)
orders = await user.orders()  # Automatic resolver
products = await orders[0].products()

🛡️ 类型安全与验证

每次交互都进行完整的 Pydantic 验证：

@app.entity()
class Order(EnrichModel):
    total: float = Field(ge=0, description="Must be positive")
    email: EmailStr = Field(description="Customer email")
    status: Literal["pending", "shipped", "delivered"]

describe_model() 会列出这些允许值，让 Agent 知道有效选项。

✏️ 可变性与 CRUD

字段默认不可变。将字段标记为可变，并使用 自动生成的补丁模型进行更新：

@app.entity()
class Customer(EnrichModel):
    id: int = Field(description="ID")
    email: str = Field(json_schema_extra={"mutable": True}, description="Email")


@app.create()
async def create_customer(email: str) -> Customer: ...


@app.update()
async def update_customer(cid: int, patch: Customer.PatchModel) -> Customer: ...


@app.delete()
async def delete_customer(cid: int) -> bool: ...

📄 内置分页

轻松处理大型数据集：

from enrichmcp import PageResult


@app.retrieve()
async def list_orders(page: int = 1, page_size: int = 50) -> PageResult[Order]:
    orders, total = await db.get_orders_page(page, page_size)
    return PageResult.create(items=orders, page=page, page_size=page_size, total_items=total)

更多示例请参阅分页指南。

🔐 上下文与身份验证

传递身份验证信息、数据库连接或任何上下文：

from pydantic import Field
from enrichmcp import EnrichModel


class UserProfile(EnrichModel):
    """User profile information."""

    user_id: int = Field(description="User ID")
    bio: str | None = Field(default=None, description="Short bio")


@app.retrieve()
async def get_user_profile(user_id: int) -> UserProfile:
    ctx = app.get_context()
    # Access context provided by MCP client
    auth_user = ctx.get("authenticated_user_id")
    if auth_user != user_id:
        raise PermissionError("Can only access your own profile")
    return await db.get_profile(user_id)

⚡ 请求缓存

将结果存储在每次请求、每个用户或全局缓存中，以减少 API 开销：

@app.retrieve()
async def get_customer(cid: int) -> Customer:
    ctx = app.get_context()

    async def fetch() -> Customer:
        return await db.get_customer(cid)

    return await ctx.cache.get_or_set(f"customer:{cid}", fetch)

🧭 参数提示

使用 EnrichParameter 为工具参数提供示例和元数据：

from enrichmcp import EnrichParameter


@app.retrieve()
async def greet_user(name: str = EnrichParameter(description="user name", examples=["bob"])) -> str:
    return f"Hello {name}"

工具描述将包含参数类型、说明和示例。

🌐 HTTP 与 SSE 支持

通过标准输出（默认方式）、SSE 或 HTTP 提供 API：

app.run()  # stdio default
app.run(transport="streamable-http")

为什么选择 EnrichMCP？

EnrichMCP 在 MCP 之上添加了三个关键层：

1. 语义层——AI Agent 能理解数据的含义，而不只是数据结构
2. 数据层——具备验证和关系功能的类型安全模型
3. 控制层——身份验证、分页和业务逻辑

最终，AI Agent 能像开发者使用 ORM 一样自然地处理您的数据。

服务端 LLM 采样

EnrichMCP 可以通过 MCP 的 sampling 功能请求语言模型补全。可从任何资源调用 ctx.ask_llm() 或其别名 ctx.sampling()，由已连接的客户端选择 LLM 并支付使用费用。你可以使用 model_preferences、allow_tools 和 max_tokens 等选项调整行为。如需了解更多 详情，请参阅 docs/server_side_llm.md。

示例

请查看示例目录：

• hello_world - 最精简的 EnrichMCP 应用
• hello_world_http - 使用 streamable HTTP 的 HTTP 示例
• shop_api - 带分页和筛选功能的内存商店 API
• shop_api_sqlite - 基于 SQLite 的版本
• shop_api_gateway - 将 EnrichMCP 作为 FastAPI 前置网关
• sqlalchemy_shop - 根据 SQLAlchemy 模型自动生成的 API
• mutable_crud - 演示可变字段和 CRUD 装饰器
• caching - 演示 ContextCache 的用法
• basic_memory - 使用 FileMemoryStore 的简单记事 API
• openai_chat_agent - 用于 MCP 示例的交互式聊天客户端

文档

• 📖 完整文档
• 🚀 入门指南
• 🔧 API 参考

贡献

欢迎贡献！详情请参阅 CONTRIBUTING.md。

开发环境设置

此代码库要求使用 Python 3.11 或更高版本。Makefile 包含用于创建虚拟环境 和运行测试的命令：

make setup            # create .venv and install dependencies
source .venv/bin/activate
make test             # run the test suite

这会安装所有开发扩展和 pre-commit 钩子，让你可以立即运行 make lint 或 make docs 等命令。

许可证

Apache 2.0 - 请参阅 LICENSE

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