api-design

name

api-design

description

This skill should be used when the user needs to "design the API", do "endpoint design", pin down a "request/response shape", choose a "pagination" strategy (cursor vs offset), add an "idempotency key" to a write, plan "API versioning", an "error contract", or pick between "REST vs gRPC vs GraphQL" or "WebSocket vs polling". Use it whenever a design has reached the interface — the concrete request, response, primary access path, and how clients page, retry, and version — even if the user only said "the boxes talk to each other".

API Design

Define the contract between clients and a service: the exact request and response shapes, how callers page through data, how retried writes stay safe, how errors are reported, and how the contract evolves. Get this vague and the rest of the diagram is guesswork (GUIDE #8) — a "NoSQL box" or "user service" solves nothing until you can write the request, the response, and the key it hits.

When to reach for this

The design has named services and a datastore, and you now need the interface: what a client sends, what it gets back, how it fetches the next page, how it retries a payment without double-charging, and how a v1 client survives a v2 deploy. Reach here the moment someone says "fetch the feed" or "store the post" without a shape.

When NOT to

Before requirements and scale are pinned — the protocol choice depends on read/write ratio and latency target, not taste (→ requirements-scoping, back-of-the-envelope). Don't reach for gRPC, GraphQL, or streaming because they sound modern; a plain REST/JSON endpoint is the cheapest contract that meets most constraints, and naming a fancier protocol you don't need is a YAGNI red flag. Internal data-access keys and partition design live in data-storage; this skill designs the external contract that mirrors them.

Clarify first

• Call shape — request/response (CRUD), bidirectional/real-time, or one request → many results (streaming)? This picks the protocol.
• Read/write ratio and result-set size — drives pagination and whether reads need their own optimized path (→ back-of-the-envelope).
• Retry safety — can a write be safely repeated? Which operations are naturally idempotent (PUT/DELETE) vs not (POST that creates/charges)?
• Client diversity & churn — public third parties (slow to upgrade, need strict versioning) vs your own apps (ship together)?
• Latency & payload budget — mobile/high-latency links favor compact binary and fewer round trips; browsers favor cacheable HTTP.

The options

Protocol / style (pick per call shape)

• REST over HTTP/JSON — resource CRUD over standard verbs. Use as the default for public, cacheable, browser-friendly APIs.
• RPC / gRPC (HTTP/2, protobuf) — typed method calls, compact binary, streaming. Use for internal service-to-service traffic where latency and schema contracts matter.
• GraphQL — client specifies exactly the fields it wants in one query. Use when many clients need different shapes of the same graph and over/under-fetching on REST hurts.
• WebSocket / SSE (streaming) — persistent server→client push. Use when the server must push updates (chat, presence, live feeds) — see the polling tier below.
• Webhooks — server calls the client's URL on an event. Use for async, third-party event delivery.

Server-push tier (when clients need fresh data)

• Polling → long-polling → SSE → WebSocket, in increasing efficiency for push and increasing connection cost. Start at polling; escalate only when a number (update frequency, fanout) forces it.

Pagination

• Cursor (keyset) — opaque token over a stable sort key. Default for large or changing datasets.
• Offset/limit — ?offset=40&limit=20. Use only for small, mostly-static, jump-to-page lists.

Idempotency — client sends an Idempotency-Key on unsafe writes; the server dedupes retries. This skill owns the key contract (see Interface sketch).

Versioning — URI (/v2/...), header (Accept: application/vnd.x.v2+json), or additive/never-break. Prefer additive; reserve a new version for breaking changes.

Trade-offs

Option	What it solves	What it worsens	Change it when
REST/JSON	Universal, cacheable, simple, tooling everywhere	Over/under-fetch; chatty for graphs; weak typing	Field-shaping pain → GraphQL; internal latency → gRPC
gRPC/RPC	Compact, typed, fast, native streaming	Not browser-native; needs proxy; opaque to HTTP caches	Public/browser clients need it → REST gateway
GraphQL	One round trip, client picks fields	Caching/rate-limiting hard; expensive queries (N+1); server complexity	Few fixed shapes (REST simpler) or query cost unbounded
WebSocket/SSE	True server push, low-latency updates	Stateful connections, harder to scale/LB, reconnection logic	Updates are infrequent → long-poll; one-way only → SSE
Cursor pagination	Stable under inserts; O(1) per page at any depth	Opaque token; no random page jump; needs sort key	Users must jump to page N of a static set → offset
Offset pagination	Trivial; arbitrary page jumps	Drift/dupes on insert; deep offsets scan & slow	Set grows or mutates → cursor
Idempotency keys	Safe retries; no double-charge	Server must store keys + dedupe; key TTL/scope to define	Op is naturally idempotent (PUT/DELETE) → may skip
URI versioning	Explicit, cache/log-visible, easy to route	Version sprawl; clients pinned forever	Changes are additive → no new version needed

Behavior under stress

The contract decides how badly a client amplifies an incident.

• Retry storms. A timed-out write that isn't idempotent gets retried and may double-execute; clients retrying in lockstep stampede a recovering service. Idempotency keys make retries safe; the backoff/jitter that paces them is owned by resilience-failure. Surface 429 + Retry-After so well-behaved clients slow down.
• Deep pagination. Offset pagination at large offsets forces the store to scan and discard rows — a cheap-looking endpoint becomes a full-table scan under a crawler. Cursor pagination keeps every page O(page-size).
• Unbounded responses. No default page size, no max payload, or a GraphQL query that fans out → one request exhausts memory/CPU. Cap page size, depth, and complexity at the contract.
• Connection exhaustion. WebSocket/SSE hold a connection per client; a reconnect storm after a deploy can exhaust file descriptors and load-balancer slots. Plan reconnect with jitter and connection limits.
• Versioning breakage. A non-additive change to a shared shape breaks every client at once — the loudest self-inflicted outage. Make changes additive; deprecate behind a version.

Monitor: error-rate by status class (4xx vs 5xx), p99 latency per endpoint, retry/idempotency-replay rate, page-depth distribution, open connection count, and per-version traffic (to know when an old version can be retired).

How to apply

1. Clarify the inputs. Pin call shape, read/write ratio, result-set size, retry safety, client diversity, and latency/payload budget (see Clarify first). No contract before these are known.
2. Pick the protocol and pagination from the trade-off table. Default to REST/JSON; escalate to gRPC (internal latency/typing), GraphQL (many field shapes), or a push tier (server must push) only when an input forces it. Default pagination to cursor; reserve offset for small, static, jump-to-page sets.
3. Set the key knobs. Choose idempotent verbs by safety (GET/PUT/DELETE safe to retry, POST not), define the Idempotency-Key contract and its TTL, fix a stable error envelope, and pick a versioning policy (additive by default).
4. Stress-test the contract. Cap default and max page size, GraphQL depth/complexity, and payload size; plan reconnect-with-jitter and connection limits for push; surface 429 + Retry-After. Confirm a v1 client survives a v2 deploy.
5. Size it with numbers. Estimate requests/s per endpoint, response size, pages-per-session, and update-frequency × fanout; use these to confirm the protocol and poll-vs-push choice (→ back-of-the-envelope).
6. Pick a provider. Keep the generic recipe unless the user names a cloud, then map to its managed gateway (see Choosing a provider).

Dos and don'ts

Do

• Default to REST/JSON and cursor pagination; escalate only when a number forces it.
• Choose verbs by retry safety so retries are correct by construction.
• Require an Idempotency-Key on every non-idempotent write and bound its TTL.
• Cap page size, query depth/complexity, and payload so worst-case cost is bounded.
• Keep one stable error envelope (code, message, request_id, retryable) across every endpoint.

Don't

• Reach for gRPC, GraphQL, or streaming because they sound modern (YAGNI red flag).
• Use offset pagination on growing or mutating sets — deep offsets scan the store.
• Ship a non-additive change to a shared shape without a new version.
• Leave responses unbounded (no default page size, no max payload).
• Re-teach retries/backoff or sharding here — link to resilience-failure / data-storage.

Numbers that matter

Estimate before choosing: requests/s per endpoint, average and max response size, page size × pages-per-session (drives read load), and update frequency × fanout (decides poll vs push). A few rules of thumb that flip a decision: a binary protocol (protobuf) commonly cuts payload several-fold over JSON, and compression helps again before the wire — both matter most on mobile/high-latency links where round trips dominate. Polling at interval T for N clients is N/T requests/s of mostly-empty answers; once that approaches the push tier's connection budget, switch to SSE/WebSocket. Cap page size (and GraphQL depth/complexity) so worst-case response cost is bounded, and keep an idempotency key's stored window bounded (e.g. 24h) so the dedupe table doesn't grow without limit. For the canonical latency/QPS/payload reference figures, go to back-of-the-envelope — don't restate its tables here.

Interface sketch

Make the contract concrete. A read with cursor pagination and an error envelope:

GET /v1/users/{id}/posts?limit=20&cursor=eyJ0cyI6MTciLCJpZCI6Ijk5In0
200 OK
{ "data": [ { "id": "p_881", "created_at": "...", "text": "..." } ],
  "next_cursor": "eyJ0cyI6...",          // null when no more pages
  "has_more": true }

# error envelope — stable shape across every endpoint
4xx/5xx
{ "error": { "code": "rate_limited", "message": "…", "request_id": "req_…",
             "retryable": true } }

An idempotent write (this skill's owned contract):

POST /v1/payments
Idempotency-Key: 9f1c-… (client-generated UUID, unique per logical operation)
{ "amount": 4200, "currency": "usd", "source": "card_…" }

# Server: first request with a key → execute, store (key → response) for a TTL.
# Retry with same key → return the stored response, do NOT re-execute.
# Same key + different body → 422 (key reuse conflict).

Mirror the response shape to the access pattern: cursor encodes the partition/sort key the store pages on (PK/SK design is owned by data-storage). Pick verbs by safety — GET (safe), PUT/DELETE (idempotent), POST (not), so retries are correct by construction.

Choosing a provider

Default to the generic recipe above. If the user names a cloud, read references/providers/<provider>.md for the managed-service mapping, quotas/limits, and provider-specific trade-offs. If no file exists for that provider, the generic recipe is the answer.

Diagram

To visualize the request/response path, the cursor-paging loop, or an idempotent-retry sequence (client → gateway → service → store, with the replay branch), use the in-plugin architecture-diagram skill — do not embed Mermaid here. A one-line ASCII sketch inline is fine for quick reasoning.

Related building blocks

• data-storage — depends on it: the request/response and cursor shapes mirror its primary key and access patterns (sharding/partitioning lives there); design them together.
• resilience-failure — pairs with it: it owns retries/backoff/jitter, timeouts, and rate limiting, while idempotency keys (owned here) are what make those retries safe.
• consistency-coordination — pairs with it when a retried or concurrent write must not violate an invariant; CAP/quorum theory lives there.
• messaging-streaming — alternative to synchronous request/response when the contract is async events or webhooks.
• system-design — feeds into this: the orchestrator routes here at the interface step.

References

• references/deep-dive.md — protocol mechanics (HTTP verbs/status discipline, gRPC streaming modes, GraphQL query-cost limits), cursor-token construction, the full idempotency-key state machine, versioning/deprecation strategy, and error-contract design. Read when designing the contract in detail.
• references/providers/{generic,aws,azure,gcp}.md — API-gateway / managed-endpoint mappings, limits that change a decision, and pitfalls per environment.