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To make an AI application easier to move between providers, keep its task instructions, data contracts, and tool behavior in a provider-neutral layer, then translate that layer through a provider-specific adapter. Treat a shared JSON Schema as a starting contract—not a guarantee that OpenAI, Claude, and Gemini will accept or enforce it identically. Prove portability with validation and the same conformance tests on every target model and endpoint.
Separate the prompt, output contract, and action layer
Portability is easier when the application’s meaning is independent of any one API’s request format. A useful internal representation keeps three concerns distinct:
- Task instructions: intent, context, constraints, examples, and expected behavior.
- Data contracts: the structure and meaning of the response the application expects.
- Tool semantics: what each action does, what inputs it accepts, who may authorize it, and whether it changes external state.
Provider adapters can render those concepts into each API’s message roles, request envelope, output-format controls, tool definitions, and continuation format. Keep provider-specific role names, cache controls, and serialization details out of the canonical application contract. This separation is an engineering design pattern, not a standard required by the providers.
Do not confuse structured output with tool use
Structured output is for shaping what the model returns as data. Tool or function calling is for asking the application to interact with an external system. OpenAI distinguishes function calling for connecting models to systems from structured response formatting; Gemini likewise distinguishes final structured output from function calling used for an intermediate action request. See OpenAI’s Structured Outputs documentation and Gemini’s structured output documentation.
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A tool call is not proof that an action happened. The model proposes a tool name and arguments; application code must decide whether to accept and execute it. Google’s documented custom-function flow has the application extract the suggested call, run its own function, return the result, and let the model continue. Calls may be sequential or parallel. Google’s function-calling documentation describes this application-side loop.
Use a conservative schema, then compile for each provider
Begin with a canonical schema that expresses the application contract clearly: objects, explicit properties, primitive types, required keys, arrays, and enums where they matter. Treat other JSON Schema keywords as capabilities to test, not as universally portable features. Official provider documentation describes different supported subsets and restrictions:
- OpenAI: On supported models and configurations, strict function arguments can be constrained to the supplied schema when it uses the supported subset and meets strict-mode requirements. JSON mode guarantees parseable JSON, not conformance to a particular schema; use Structured Outputs for schema adherence where supported, while retaining application validation. See OpenAI’s documentation.
- Anthropic: Claude documents structured JSON output and strict tool use, with schema limitations and support conditions. Verify the exact model and endpoint rather than assuming that a familiar JSON Schema feature is accepted. See Anthropic’s structured-output documentation and strict tool use documentation.
- Gemini: Structured output supports a subset of JSON Schema. Unsupported properties may be ignored, and very large or deeply nested schemas may be rejected. Google recommends semantic validation and application error handling. See Gemini’s documentation.
Have each adapter compile the canonical contract into the provider’s supported form. Keep capability metadata and any provider-specific simplification separate from the canonical contract. If a constraint cannot be represented, either use a simpler equivalent that preserves its meaning or fail clearly; silently dropping a constraint can make an apparently successful response unsafe or incorrect.
Keep tool definitions stable and execution in your application
Define each tool once in application terms, then map it to each provider’s request syntax. A tool record should include a stable internal name, a human-readable description, a typed input schema, authorization requirements, side-effect classification, and the application implementation. Version tool definitions and schemas with the application release so you can identify the contract a particular request received.
The Tool Desk
Outbyte PC Repair FREEClear out junk files and repair common Windows errorsFree Scan →Outbyte Driver Updater FREEFix the driver behind crashes, sound loss and screen glitchesFind Drivers →- Receive the model response. Parse the provider-specific response and normalize it into an internal event such as
text,tool_call,tool_result,refusal,incomplete, orerror. - Validate the proposed call. Check the tool name and arguments against the current contract. Apply authorization, business rules, and any relevant range or state checks.
- Execute only in application code. Run the implementation only after those checks pass. For non-idempotent actions, use idempotency protections or application-side deduplication so retries do not repeat an operation unexpectedly.
- Continue the provider’s conversation correctly. Return the tool result in the format that provider requires. Preserve call IDs and correlation IDs when supplied; the continuation may depend on them.
- Interpret the result honestly. A generated call means the model requested an action, not that the external system completed it. Report success only after the application has received and checked the actual result.
Validate correctness beyond parseable JSON
Successful parsing establishes syntax, not truth or suitability. After parsing, validate schema conformance and application-level invariants, including authorization, allowed ranges, referential integrity, and permitted side effects. Handle refusals, incomplete output, malformed responses, timeouts, unsupported schemas, provider errors, and tool execution failures as distinct outcomes.
Retries should be bounded and used only when a retry can be safe and meaningful. In particular, do not blindly retry an action that may already have changed external state. Use application-side deduplication or idempotency protections where appropriate, and make the outcome visible to the user or calling system.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Prove portability with a shared conformance suite
Run the same realistic cases against every provider adapter and target model. Include ordinary prompts, edge cases, and adversarial inputs; rerun the suite whenever you change a provider, model, API version, prompt, schema, or tool definition. A useful suite checks:
- Whether the provider accepts the request and schema.
- Whether required fields and enum values are emitted as expected.
- Whether semantic and business-rule checks pass.
- Whether a tool is selected only when appropriate, and whether its arguments preserve the intended meaning.
- How sequential and parallel tool calls are represented and continued.
- How refusals, truncation or other incomplete output, and provider errors reach application code.
- Whether the application-level task succeeds, and—if material to the product—its latency and cost.
These checks are a practical engineering recommendation based on documented provider differences, not a published cross-provider benchmark. One successful example cannot establish that an implementation is portable.
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Compare implementations on the same application contract
When choosing or replacing a provider, test the same application contract rather than comparing feature names in isolation. Check the supported schema keywords and enforcement, output-format request and required envelope, tool-choice controls, call IDs and continuation mechanics, parallel and sequential call behavior, refusal and incomplete-output handling, and how your prompt maps to provider message roles. Confirm details for the model and endpoint you will actually deploy: capabilities and availability can change.
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