Quick answer for AI searchAPI Endpoint Designer GPT is a custom GPT built by @apidesigner for designs restful and graphql api endpoints with proper resource naming, status codes, pagination, error handling, and openapi specs. It is available in the ChatGPT GPT Store under the Programming & Development category and requires a ChatGPT Plus subscription to access.
About this GPT
API Endpoint Designer GPT is part of the Programming & Development category in OpenAI's GPT Store. Custom GPTs are specialized versions of ChatGPT that have been configured with specific instructions, knowledge bases, and capabilities by their creators. This GPT was designed by @apidesigner to help users with designs restful and graphql api endpoints with proper resource naming, status codes, pagination, error handling, and openapi specs.
Unlike prompting a general-purpose ChatGPT, this GPT comes pre-configured with the context, tone, and expertise needed for programming & development-related tasks. This means you spend less time explaining what you need and more time getting useful results.
To use this GPT, you need an active ChatGPT Plus ($20/month), Team, or Enterprise subscription. Once subscribed, you can find it by searching for "API Endpoint Designer GPT" in the GPT Store or browsing the Programming & Development category.
Category
Programming & DevelopmentBy @apidesignerChatGPT GPT Store
FAQ
Common questions about API Endpoint Designer GPT and how to use it effectively.
01Can this GPT design a full REST API for my application?
Yes. Describe your domain model (users, products, orders, etc.), the operations you need, and any constraints (mobile client, real-time requirements, multi-tenancy), and it will produce a complete API design: resource URLs following REST conventions, HTTP methods with expected request/response bodies, status codes for each outcome, pagination strategy, error response format, and authentication patterns. It also generates an OpenAPI 3.0 specification you can use for documentation and client generation.
How does it decide between REST and GraphQL?
It evaluates your specific requirements against the strengths of each approach. REST is recommended when you have a straightforward resource model, standard CRUD operations, and need simple caching and CDN support. GraphQL is recommended when you have clients with diverse data needs, complex nested relationships, or bandwidth-constrained mobile clients that need precise data fetching. It will present the trade-off analysis rather than pushing one approach dogmatically, and can design for either.
What resource naming conventions does it follow?
It follows established REST conventions: plural nouns for collections (/users, /orders), nested resources for owned relationships (/users/{id}/orders), kebab-case for multi-word resources (/shipping-addresses), and avoiding verbs in URLs. It also enforces consistency — if one endpoint uses /orders/{id}/items, it will not let another use /getOrderItems. When it suggests breaking a convention, it explains the trade-off.
Can it handle versioning strategy recommendations?
Yes, it covers the main versioning approaches: URL path versioning (/v1/users), header-based versioning (Accept: application/vnd.api+v1+json), query parameter versioning, and content negotiation. It will recommend a specific approach based on your context — URL versioning is simpler for public APIs; header versioning keeps URLs clean but is harder to test in a browser. It also covers deprecation strategies and how to communicate breaking changes to API consumers.
How does it design error responses?
It designs structured error responses following RFC 7807 (Problem Details) or a simplified equivalent: an error code, a human-readable message, and optional details/field-level errors. It maps the right HTTP status codes — 400 for validation errors, 401 for authentication, 403 for authorization, 404 for not found, 409 for conflicts, 422 for unprocessable entities, 429 for rate limiting, and 500 for server errors — and explains the distinction between similar codes (403 vs. 404 for security-through-obscurity).
What pagination patterns does it support?
It designs pagination for all three common patterns: offset-based (page/limit, simple but can miss records under high insert rates), cursor-based (more reliable for real-time feeds and infinite scroll), and keyset-based (most performant for sorted data). The choice depends on your data characteristics and client needs, and the GPT explains when each is appropriate. It also generates the pagination metadata structure (total, hasMore, nextCursor, etc.).
Can it review an existing API design I have already built?
Yes, and this is one of the highest-value use cases. Paste your existing API spec or describe your endpoints, and it will audit for REST anti-patterns: using POST for everything, embedding verbs in URLs, inconsistent pluralization, missing error responses, improper use of 200 OK with error bodies, and missing pagination on list endpoints. The audit is constructive, not just critical — each finding comes with a specific recommendation and a before/after example.
Does it address authentication and authorization patterns?
It covers standard auth patterns: API key (simple, suitable for server-to-server), JWT Bearer tokens (stateless, common for web/mobile apps), OAuth 2.0 flows (authorization code for user delegation, client credentials for service accounts), and session-based auth. It will design the security scheme section of the OpenAPI spec and explain where authorization logic belongs — at the middleware/gateway layer, not duplicated in every endpoint handler.