Networking

AI API calls cost $0.01-$0.10+ each. Systems MUST treat AI calls as a managed resource with budgets, caching, and fallback strategies.

Use REST with consistent conventions. Follow the platform API guidelines (Microsoft, Google,

Parse protocol and API inputs strictly, reject violations loudly, and actively co-evolve spec and implementation instead of tolerating drift.

Use HTTP caching headers. The server controls cache policy; the client honors it.

Use [RFC 9457 Problem Details](https://www.rfc-editor.org/rfc/rfc9457) format with

Use ETags with If-None-Match for efficient reads and If-Match for optimistic concurrency on writes to prevent lost updates.

Accept a client-supplied Idempotency-Key on write endpoints so retries are safe, and reject key reuse with different parameters.

Design MCP servers by choosing the right primitive and giving each tool a precise name, output schema, and annotations.

For apps that must work offline, design for local-first with background sync.

Prefer **cursor pagination** for most APIs — stable under concurrent mutations, consistent

Respect server rate limits. Handle 429 responses gracefully.

Choose the simplest technique that meets your needs.

Not every failure is permanent. Retry transient failures with exponential backoff and jitter.

Always set both connection and read timeouts. Never use infinite timeouts.

Use Flask for web services. The dashboard service runs on Flask with a REST API and SSE/polling for live updates.