petrogurcak/fastapi-workflow

FastAPI Workflow-Driven Development Skill

CRITICAL PRINCIPLE

DOCS FIRST, CODE SECOND

You MUST NEVER generate FastAPI code without first fetching relevant documentation via MCP tools.

This is NON-NEGOTIABLE. Every workflow below has MANDATORY MCP fetch steps that MUST be completed before implementation.

When to Use This Skill

Use this skill for ALL FastAPI development tasks:

• Creating API endpoints (routers)
• Defining Pydantic models (request/response)
• Implementing dependency injection
• Database operations (SQLAlchemy)
• Authentication/authorization
• Background tasks
• WebSocket endpoints
• Any Python backend code with FastAPI

Current Stack Requirements

Required Versions:

• Python 3.11+ (3.12+ recommended)
• FastAPI 0.124+
• Pydantic 2.12+ (v2 is default, v1 support is DEPRECATED)
• SQLAlchemy 2.0.44+ (async support)
• Uvicorn (ASGI server)
• scalar-fastapi (API docs — replaces Swagger UI and ReDoc)

DEPRECATED:

• Pydantic v1 compatibility will be removed in future FastAPI versions

Key Pydantic v2 Changes:

• model_validator instead of validator
• field_validator instead of validator
• ConfigDict instead of class Config
• Performance boost (Rust core)
• Strict mode available

API Documentation Setup (Scalar)

ALWAYS use Scalar instead of Swagger UI / ReDoc. Scalar provides modern, interactive API docs with dark mode, search, and code snippets.

Setup pattern:

# requirements.txt
scalar-fastapi>=1.6.0

# main.py
from scalar_fastapi import get_scalar_api_reference

app = FastAPI(
    title="My API",
    docs_url=None,   # Disable Swagger UI
    redoc_url=None,  # Disable ReDoc
)

@app.get("/docs", include_in_schema=False)
async def scalar_docs():
    return get_scalar_api_reference(
        openapi_url=app.openapi_url,
        title="My API",
        dark_mode=True,
    )

Key points:

• Disable built-in docs_url and redoc_url in FastAPI constructor
• Serve Scalar on /docs as a custom route
• include_in_schema=False hides the docs endpoint from the API schema
• OpenAPI JSON stays at /openapi.json (FastAPI default)

Core Workflows

Workflow 1: Creating API Endpoints

Use for: Creating new API routes/endpoints

MANDATORY CHECKLIST:

☐ 1. Determine endpoint type
     - GET (retrieve data)
     - POST (create data)
     - PUT/PATCH (update data)
     - DELETE (remove data)

☐ 2. ⚠️ MANDATORY: Fetch FastAPI routing docs
     Call MCP tool: fetch_fastapi_docs(topic: "routing")
     Wait for response before continuing

☐ 3. ⚠️ MANDATORY: Fetch Pydantic model docs
     Call MCP tool: fetch_pydantic_docs(topic: "models")
     Wait for response before continuing

☐ 4. Define Pydantic models:
     - Request body model (for POST/PUT)
     - Response model
     - Use Pydantic v2 syntax

☐ 5. Implement endpoint:
     - Use APIRouter for organization
     - Proper HTTP status codes
     - Response model in decorator
     - Async def for async operations

☐ 6. Add dependencies:
     - Database session
     - Authentication
     - Rate limiting (if needed)

☐ 7. VERIFY against quality checklist:
     ✓ Uses FastAPI 0.115+ patterns
     ✓ Pydantic v2 models
     ✓ Proper error handling
     ✓ OpenAPI docs generated
     ✓ Type hints everywhere

Example Execution:

# WRONG: Generating code without fetching docs
@app.get("/products")
def get_products():
    return products

# CORRECT: Workflow followed
# 1. Determined: GET endpoint for products
# 2. Called: fetch_fastapi_docs(topic: "routing")
# 3. Called: fetch_pydantic_docs(topic: "models")
# 4. Reviewed current patterns from docs
# 5. Now implementing with current API:

from fastapi import APIRouter, Depends, HTTPException, status
from pydantic import BaseModel, ConfigDict
from typing import Annotated

router = APIRouter(prefix="/products", tags=["products"])

class ProductResponse(BaseModel):
    model_config = ConfigDict(from_attributes=True)

    id: int
    name: str
    price: float
    description: str | None = None

@router.get(
    "/",
    response_model=list[ProductResponse],
    summary="Get all products",
)
async def get_products(
    db: Annotated[AsyncSession, Depends(get_db)],
    skip: int = 0,
    limit: int = 100,
) -> list[ProductResponse]:
    """Retrieve all products with pagination."""
    products = await db.execute(
        select(Product).offset(skip).limit(limit)
    )
    return products.scalars().all()

Workflow 2: Pydantic Models

Use for: Defining request/response schemas

MANDATORY CHECKLIST:

☐ 1. Identify model purpose:
     - Request body (input validation)
     - Response model (output serialization)
     - Database model (ORM mapping)
     - Internal model (business logic)

☐ 2. ⚠️ MANDATORY: Fetch Pydantic v2 documentation
     Call MCP tool: fetch_pydantic_docs(topic: "models")
     For validators: fetch_pydantic_docs(topic: "validators")
     Wait for response before continuing

☐ 3. Define model with Pydantic v2 syntax:
     - Use ConfigDict instead of class Config
     - Use field_validator instead of validator
     - Use model_validator for cross-field validation
     - Proper type hints (str | None, not Optional[str])

☐ 4. Add validation:
     - Field constraints (min_length, ge, le)
     - Custom validators
     - Computed fields if needed

☐ 5. Handle serialization:
     - from_attributes=True for ORM
     - alias for field name mapping
     - exclude for sensitive fields

☐ 6. VERIFY model:
     ✓ Pydantic v2 syntax (not v1)
     ✓ All fields typed
     ✓ Validation covers edge cases
     ✓ Serialization correct

Example - Pydantic v2 Models:

# CORRECT: Pydantic v2 patterns
from pydantic import BaseModel, ConfigDict, Field, field_validator, model_validator
from datetime import datetime

class ProductCreate(BaseModel):
    """Request model for creating a product."""
    name: str = Field(min_length=1, max_length=100)
    price: float = Field(gt=0, description="Price must be positive")
    description: str | None = None
    category_id: int

    @field_validator('name')
    @classmethod
    def name_must_be_title_case(cls, v: str) -> str:
        return v.strip().title()

class ProductResponse(BaseModel):
    """Response model for product data."""
    model_config = ConfigDict(
        from_attributes=True,  # Enable ORM mode
        json_schema_extra={
            "example": {
                "id": 1,
                "name": "Example Product",
                "price": 29.99,
            }
        }
    )

    id: int
    name: str
    price: float
    description: str | None = None
    created_at: datetime

class ProductUpdate(BaseModel):
    """Request model for updating a product."""
    name: str | None = None
    price: float | None = Field(default=None, gt=0)
    description: str | None = None

    @model_validator(mode='after')
    def check_at_least_one_field(self) -> 'ProductUpdate':
        if all(v is None for v in [self.name, self.price, self.description]):
            raise ValueError('At least one field must be provided')
        return self

Workflow 3: Dependency Injection

Use for: Reusable dependencies (database, auth, etc.)

MANDATORY CHECKLIST:

☐ 1. Identify dependency type:
     - Database session
     - Current user (authentication)
     - Configuration settings
     - External service client

☐ 2. ⚠️ MANDATORY: Fetch FastAPI dependency docs
     Call MCP tool: fetch_fastapi_docs(topic: "dependencies")
     Wait for response before continuing

☐ 3. Implement dependency function:
     - Use Annotated for type hints
     - Yield for cleanup (database sessions)
     - Async for async operations

☐ 4. Use in endpoints:
     - Depends() for injection
     - Annotated[Type, Depends(dep)] pattern

☐ 5. VERIFY dependency:
     ✓ Proper cleanup (yield)
     ✓ Error handling
     ✓ Reusable across endpoints

Example - Dependencies:

from fastapi import Depends, HTTPException, status
from fastapi.security import OAuth2PasswordBearer
from sqlalchemy.ext.asyncio import AsyncSession
from typing import Annotated

# Database dependency
async def get_db() -> AsyncGenerator[AsyncSession, None]:
    async with async_session() as session:
        try:
            yield session
            await session.commit()
        except Exception:
            await session.rollback()
            raise

# Type alias for reuse
DbSession = Annotated[AsyncSession, Depends(get_db)]

# Auth dependency
oauth2_scheme = OAuth2PasswordBearer(tokenUrl="auth/token")

async def get_current_user(
    token: Annotated[str, Depends(oauth2_scheme)],
    db: DbSession,
) -> User:
    credentials_exception = HTTPException(
        status_code=status.HTTP_401_UNAUTHORIZED,
        detail="Could not validate credentials",
        headers={"WWW-Authenticate": "Bearer"},
    )
    try:
        payload = jwt.decode(token, SECRET_KEY, algorithms=[ALGORITHM])
        user_id: int = payload.get("sub")
        if user_id is None:
            raise credentials_exception
    except JWTError:
        raise credentials_exception

    user = await db.get(User, user_id)
    if user is None:
        raise credentials_exception
    return user

# Type alias for authenticated endpoints
CurrentUser = Annotated[User, Depends(get_current_user)]

# Usage in endpoint
@router.get("/me")
async def get_me(current_user: CurrentUser) -> UserResponse:
    return current_user

Workflow 4: Database Operations (SQLAlchemy 2.0)

Use for: Database models and queries

MANDATORY CHECKLIST:

☐ 1. Determine database operation:
     - Model definition
     - CRUD operations
     - Complex queries
     - Migrations

☐ 2. ⚠️ MANDATORY: Fetch SQLAlchemy 2.0 docs
     Call MCP tool: fetch_sqlalchemy_docs(topic: "async")
     Wait for response before continuing

☐ 3. Define SQLAlchemy model:
     - Use Mapped and mapped_column (SQLAlchemy 2.0)
     - Relationships with proper loading
     - Indexes for performance

☐ 4. Implement repository/service:
     - Async operations
     - Proper transaction handling
     - Error handling

☐ 5. VERIFY database code:
     ✓ SQLAlchemy 2.0 syntax
     ✓ Async operations
     ✓ N+1 queries avoided
     ✓ Transactions handled

Example - SQLAlchemy 2.0:

from sqlalchemy import ForeignKey, String, select
from sqlalchemy.ext.asyncio import AsyncSession, create_async_engine
from sqlalchemy.orm import DeclarativeBase, Mapped, mapped_column, relationship

class Base(DeclarativeBase):
    pass

class Product(Base):
    __tablename__ = "products"

    id: Mapped[int] = mapped_column(primary_key=True)
    name: Mapped[str] = mapped_column(String(100))
    price: Mapped[float]
    description: Mapped[str | None]
    category_id: Mapped[int] = mapped_column(ForeignKey("categories.id"))

    category: Mapped["Category"] = relationship(back_populates="products")

class Category(Base):
    __tablename__ = "categories"

    id: Mapped[int] = mapped_column(primary_key=True)
    name: Mapped[str] = mapped_column(String(50), unique=True)

    products: Mapped[list["Product"]] = relationship(back_populates="category")

# Repository pattern
class ProductRepository:
    def __init__(self, session: AsyncSession):
        self.session = session

    async def get_all(self, skip: int = 0, limit: int = 100) -> list[Product]:
        result = await self.session.execute(
            select(Product)
            .options(selectinload(Product.category))
            .offset(skip)
            .limit(limit)
        )
        return list(result.scalars().all())

    async def get_by_id(self, product_id: int) -> Product | None:
        return await self.session.get(Product, product_id)

    async def create(self, product_data: ProductCreate) -> Product:
        product = Product(**product_data.model_dump())
        self.session.add(product)
        await self.session.flush()
        return product

Workflow 5: Error Handling

Use for: API error responses

MANDATORY CHECKLIST:

☐ 1. ⚠️ MANDATORY: Fetch FastAPI error handling docs
     Call MCP tool: fetch_fastapi_docs(topic: "error-handling")
     Wait for response before continuing

☐ 2. Define custom exceptions:
     - Inherit from Exception
     - Include error details

☐ 3. Create exception handlers:
     - Register with app
     - Return proper HTTP responses

☐ 4. Use HTTPException for simple cases:
     - Proper status codes
     - Clear error messages

☐ 5. VERIFY error handling:
     ✓ All errors return JSON
     ✓ Proper status codes
     ✓ No sensitive info leaked

Example - Error Handling:

from fastapi import FastAPI, HTTPException, Request, status
from fastapi.responses import JSONResponse

# Custom exception
class ProductNotFoundError(Exception):
    def __init__(self, product_id: int):
        self.product_id = product_id

# Exception handler
@app.exception_handler(ProductNotFoundError)
async def product_not_found_handler(
    request: Request,
    exc: ProductNotFoundError,
) -> JSONResponse:
    return JSONResponse(
        status_code=status.HTTP_404_NOT_FOUND,
        content={
            "detail": f"Product with id {exc.product_id} not found",
            "error_code": "PRODUCT_NOT_FOUND",
        },
    )

# Usage in endpoint
@router.get("/{product_id}")
async def get_product(product_id: int, db: DbSession) -> ProductResponse:
    product = await db.get(Product, product_id)
    if product is None:
        raise ProductNotFoundError(product_id)
    return product

Workflow 6: Testing

Use for: API tests with pytest

MANDATORY CHECKLIST:

☐ 1. ⚠️ MANDATORY: Fetch FastAPI testing docs
     Call MCP tool: fetch_fastapi_docs(topic: "testing")
     Wait for response before continuing

☐ 2. Set up test client:
     - Use TestClient or httpx.AsyncClient
     - Override dependencies for testing

☐ 3. Write tests:
     - Test happy path
     - Test error cases
     - Test edge cases

☐ 4. Use fixtures:
     - Database fixtures
     - Auth fixtures
     - Factory patterns

☐ 5. VERIFY tests:
     ✓ All endpoints tested
     ✓ Error cases covered
     ✓ No real DB in unit tests

Example - Testing:

import pytest
from httpx import AsyncClient, ASGITransport
from sqlalchemy.ext.asyncio import create_async_engine, AsyncSession

@pytest.fixture
async def client(db_session: AsyncSession):
    """Test client with overridden dependencies."""
    def override_get_db():
        yield db_session

    app.dependency_overrides[get_db] = override_get_db

    async with AsyncClient(
        transport=ASGITransport(app=app),
        base_url="http://test",
    ) as client:
        yield client

    app.dependency_overrides.clear()

@pytest.mark.asyncio
async def test_get_products(client: AsyncClient, sample_products):
    response = await client.get("/products/")
    assert response.status_code == 200
    data = response.json()
    assert len(data) == len(sample_products)

@pytest.mark.asyncio
async def test_get_product_not_found(client: AsyncClient):
    response = await client.get("/products/99999")
    assert response.status_code == 404
    assert response.json()["error_code"] == "PRODUCT_NOT_FOUND"

@pytest.mark.asyncio
async def test_create_product(client: AsyncClient, auth_headers):
    payload = {"name": "New Product", "price": 29.99, "category_id": 1}
    response = await client.post(
        "/products/",
        json=payload,
        headers=auth_headers,
    )
    assert response.status_code == 201
    data = response.json()
    assert data["name"] == "New Product"

Code Quality Requirements

Python 3.11+ Features (MANDATORY)

# ✓ CORRECT: Modern Python
# Union types with |
name: str | None = None

# Match statements
match status:
    case "active":
        return "Active"
    case "pending":
        return "Pending"
    case _:
        return "Unknown"

# Exception groups (if needed)
try:
    async with asyncio.TaskGroup() as tg:
        tg.create_task(task1())
        tg.create_task(task2())
except* ValueError as eg:
    handle_errors(eg.exceptions)

FastAPI Patterns (MANDATORY)

# ✓ CORRECT: Annotated dependencies
from typing import Annotated

DbSession = Annotated[AsyncSession, Depends(get_db)]

@router.get("/items")
async def get_items(db: DbSession) -> list[ItemResponse]:
    ...

# ✓ CORRECT: Response model in decorator
@router.post("/items", response_model=ItemResponse, status_code=201)
async def create_item(...):
    ...

# ✓ CORRECT: Tags and summary for OpenAPI
@router.get("/items", tags=["items"], summary="List all items")
async def list_items(...):
    """
    Retrieve all items.

    - **skip**: Number of items to skip
    - **limit**: Maximum number of items to return
    """
    ...

Pydantic v2 Patterns (MANDATORY)

# ✓ CORRECT: Pydantic v2
from pydantic import BaseModel, ConfigDict, Field, field_validator

class ItemModel(BaseModel):
    model_config = ConfigDict(
        from_attributes=True,
        str_strip_whitespace=True,
    )

    name: str = Field(min_length=1)
    price: float = Field(gt=0)

    @field_validator('name')
    @classmethod
    def validate_name(cls, v: str) -> str:
        return v.title()

# ✗ WRONG: Pydantic v1 (deprecated)
class ItemModel(BaseModel):
    class Config:  # Old style
        orm_mode = True

    @validator('name')  # Old decorator
    def validate_name(cls, v):
        return v.title()

Decision Trees

Endpoint Type Selection

What operation?
├─ Retrieve data → GET
├─ Create new resource → POST
├─ Full update → PUT
├─ Partial update → PATCH
└─ Remove resource → DELETE

Response Model Selection

What are you returning?
├─ Single item → ItemResponse
├─ List of items → list[ItemResponse]
├─ Paginated → PaginatedResponse[ItemResponse]
├─ Nothing → None (with 204)
└─ Created item → ItemResponse (with 201)

Database Pattern Selection

Query complexity?
├─ Simple CRUD → Direct SQLAlchemy in endpoint
├─ Complex queries → Repository pattern
├─ Business logic → Service layer + Repository
└─ Multiple models → Unit of Work pattern

Common Mistakes to Avoid

DON'T:

• Generate code without fetching docs first
• Use Pydantic v1 syntax (validator, class Config)
• Use sync database operations
• Skip type hints
• Hardcode secrets in code
• Return ORM models directly (use response models)
• Ignore error handling
• Skip tests

DO:

• ALWAYS fetch documentation before implementing
• Follow workflows step-by-step
• Use Pydantic v2 patterns
• Use async/await for I/O operations
• Use environment variables for config
• Define response models
• Handle all error cases
• Write tests first (TDD)

Integration with MCP Server

This skill works with the FastAPI MCP server. MCP provides current docs, this skill enforces the workflow.

Available MCP Tools:

1. fetch_fastapi_docs(topic) - FastAPI patterns and features
2. fetch_pydantic_docs(topic) - Pydantic v2 models and validation
3. fetch_sqlalchemy_docs(topic) - SQLAlchemy 2.0 async operations
4. fetch_python_docs(topic) - Python stdlib features
5. search_pypi(query) - Search Python packages

You MUST call these tools at the workflow steps marked with ⚠️ MANDATORY

Verification Before Completion

Before considering any FastAPI implementation complete:

FINAL VERIFICATION CHECKLIST:
☐ All MANDATORY MCP tool calls were made
☐ Implementation follows fetched documentation
☐ Uses FastAPI 0.115+ patterns
☐ Uses Pydantic v2 syntax
☐ Uses SQLAlchemy 2.0 (if database)
☐ Python 3.11+ features used
☐ All endpoints have response models
☐ Error handling implemented
☐ Type hints everywhere
☐ Tests written and passing
☐ No secrets in code

Summary

This skill enforces a DOCS-FIRST development process for FastAPI:

1. Identify task → Choose workflow
2. Fetch docs → Call MCP tools (MANDATORY)
3. Review docs → Understand current API
4. Implement → Follow fetched patterns exactly
5. Verify → Check against quality checklist

NEVER skip step 2. The MCP server provides current, accurate documentation. Use it.

Your workflows are your process. Follow them rigorously for consistent, high-quality Python backend code that uses current FastAPI, Pydantic v2, and SQLAlchemy 2.0 patterns.