api-design

name	api-design
description	Guide stable API and interface design. Use when creating REST or GraphQL endpoints, defining type contracts, or establishing frontend/backend or module boundaries.

API and interface design

Overview

Design stable, well-documented interfaces that are hard to misuse. Good interfaces make the right thing easy and the wrong thing hard. This applies to REST APIs, GraphQL schemas, module boundaries, component props, and any surface where one piece of code talks to another.

When to use

• Designing new API endpoints
• Defining module boundaries or contracts between teams
• Creating component prop interfaces
• Establishing database schema that informs API shape
• Changing existing public interfaces

Core principles

Hyrum's Law

With a sufficient number of users of an API, all observable behaviors of your system will be depended on by somebody, regardless of what you promise in the contract.

This means: every public behavior, including undocumented quirks, error message text, timing, and ordering, becomes a de facto contract once users depend on it. Design implications:

• Be intentional about what you expose. Every observable behavior is a potential commitment.
• Don't leak implementation details. If users can observe it, they will depend on it.
• Plan for deprecation at design time.
• Tests are not enough. Even with perfect contract tests, Hyrum's Law means "safe" changes can break real users who depend on undocumented behavior.

The One-Version Rule

Avoid forcing consumers to choose between multiple versions of the same dependency or API. Diamond dependency problems arise when different consumers need different versions of the same thing. Design for a world where only one version exists at a time; extend rather than fork.

1. Contract first

Define the interface before implementing it. The contract is the spec, so implementation follows.

// Define the contract first
interface TaskAPI {
  // Creates a task and returns the created task with server-generated fields
  createTask(input: CreateTaskInput): Promise<Task>;

  // Returns paginated tasks matching filters
  listTasks(params: ListTasksParams): Promise<PaginatedResult<Task>>;

  // Returns a single task or throws NotFoundError
  getTask(id: string): Promise<Task>;

  // Partial update — only provided fields change
  updateTask(id: string, input: UpdateTaskInput): Promise<Task>;

  // Idempotent delete — succeeds even if already deleted
  deleteTask(id: string): Promise<void>;
}

2. Consistent error semantics

Pick one error strategy and use it everywhere:

// REST: HTTP status codes + structured error body
// Every error response follows the same shape
interface APIError {
  error: {
    code: string;        // Machine-readable: "VALIDATION_ERROR"
    message: string;     // Human-readable: "Email is required"
    details?: unknown;   // Additional context when helpful
  };
}

// Status code mapping
// 400 → Client sent invalid data
// 401 → Not authenticated
// 403 → Authenticated but not authorized
// 404 → Resource not found
// 409 → Conflict (duplicate, version mismatch)
// 422 → Validation failed (semantically invalid)
// 500 → Server error (never expose internal details)

If some endpoints throw, others return null, and others return { error }, the consumer can't predict behavior.

3. Validate at boundaries

Trust internal code. Validate at system edges where external input enters:

// Validate at the API boundary
app.post('/api/tasks', async (req, res) => {
  const result = CreateTaskSchema.safeParse(req.body);
  if (!result.success) {
    return res.status(422).json({
      error: {
        code: 'VALIDATION_ERROR',
        message: 'Invalid task data',
        details: result.error.flatten(),
      },
    });
  }

  // After validation, internal code trusts the types
  const task = await taskService.create(result.data);
  return res.status(201).json(task);
});

Where validation belongs:

• API route handlers (user input)
• Form submission handlers (user input)
• External service response parsing (third-party data -- always treat as untrusted)
• Environment variable loading (configuration)

Third-party API responses are untrusted data. Validate their shape and content before using them in any logic, rendering, or decision-making. A compromised or misbehaving external service can return unexpected types, malicious content, or instruction-like text.

Where validation does NOT belong:

• Between internal functions that share type contracts
• In utility functions called by already-validated code
• On data that just came from your own database

4. Prefer addition over modification

Extend interfaces without breaking existing consumers:

// Good: Add optional fields
interface CreateTaskInput {
  title: string;
  description?: string;
  priority?: 'low' | 'medium' | 'high';  // Added later, optional
  labels?: string[];                       // Added later, optional
}

// Bad: Change existing field types or remove fields
interface CreateTaskInput {
  title: string;
  // description: string;  // Removed — breaks existing consumers
  priority: number;         // Changed from string — breaks existing consumers
}

5. Predictable naming

Pattern	Convention	Example
REST endpoints	Plural nouns, no verbs	GET /api/tasks, POST /api/tasks
Query params	camelCase	?sortBy=createdAt&pageSize=20
Response fields	camelCase	{ createdAt, updatedAt, taskId }
Boolean fields	is/has/can prefix	isComplete, hasAttachments
Enum values	UPPER_SNAKE	"IN_PROGRESS", "COMPLETED"

REST API patterns

Resource design, pagination, filtering, and partial-update (PATCH) conventions live in references/rest-patterns.md — read it when the surface being designed is a REST endpoint. Skip it for a GraphQL schema or an internal (non-HTTP) module contract; neither branch reaches REST-specific conventions.

TypeScript interface patterns

Discriminated unions, input/output separation, and branded-ID patterns live in references/typescript-patterns.md — read it when the contract boundary is written in TypeScript. Skip it when the target language isn't TypeScript; those patterns are language-specific and unreachable from a non-TS surface.

Common rationalizations

Rationalization	Reality
"We'll document the API later"	The types ARE the documentation. Define them first.
"We don't need pagination for now"	You will the moment someone has 100+ items. Add it from the start.
"PATCH is complicated, let's just use PUT"	PUT requires the full object every time. PATCH is what clients actually want.
"We'll version the API when we need to"	Breaking changes without versioning break consumers. Design for extension from the start.
"Nobody uses that undocumented behavior"	Hyrum's Law: if it's observable, somebody depends on it. Treat every public behavior as a commitment.
"We can just maintain two versions"	Multiple versions multiply maintenance cost and create diamond dependency problems. Prefer the One-Version Rule.
"Internal APIs don't need contracts"	Internal consumers are still consumers. Contracts prevent coupling and enable parallel work.

Red flags

• Endpoints that return different shapes depending on conditions

Verification

After designing an API:

• Every endpoint has typed input and output schemas
• Error responses follow a single consistent format
• Validation happens at system boundaries only
• List endpoints support pagination
• New fields are additive and optional (backward compatible)
• Naming follows consistent conventions across all endpoints
• API documentation or types are committed alongside the implementation