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openai_create_response

Create AI responses using OpenAI's Responses API with support for model variants, background tasks, and search preview. Accepts conversation input and returns output with usage data.

Instructions

Create a response using the OpenAI Responses API via AceDataCloud.

The Responses API is an alternative to the Chat Completions API with support
for a wider range of model variants and additional features like background processing.

Use this when:
- You need access to model-specific dated variants (e.g., o3-2025-04-16)
- You want background processing with a task ID
- You need access to search-preview models

Returns:
    JSON response containing the model's output and usage information.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
nNoNumber of response choices to generate. Default is 1.
inputYesA list of messages comprising the conversation. Each message must have a 'role' ('system', 'user', or 'assistant') and 'content' field. Example: [{'role': 'user', 'content': 'Explain quantum computing'}]
modelNoThe model to use. Supports a wide range of GPT-4, GPT-4o, GPT-5, and o-series models including their dated variants. Default is gpt-4.1.gpt-4.1
backgroundNoWhether to run the model response in the background. When True, returns immediately with a task ID.
max_tokensNoThe maximum number of tokens to generate in the response. If not specified, the model uses its default limit.
temperatureNoSampling temperature between 0 and 2. Higher values produce more creative output; lower values produce more deterministic output. Default is 1.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations provided, so description carries full burden. It discloses background processing behavior (returns immediately with task ID) and return format (JSON with output and usage). Could add more about idempotency or rate limits.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Highly concise: three sentences plus bullet list. No unnecessary words, front-loaded with purpose, then usage, then return info.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given an output schema exists (context signal), description adequately covers purpose, usage, and behavioral traits. All 6 parameters are described in schema, and description complements with use-case context.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, so baseline is 3. The description adds context (e.g., background ties to task ID, model variants mention search-preview), but does not significantly augment parameter meaning beyond schema descriptions.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool creates a response using the OpenAI Responses API, distinguishes it from Chat Completions, and lists specific use cases like accessing dated model variants and background processing.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The 'Use this when' section provides three explicit scenarios, including background processing and search-preview models. It implicitly contrasts with Chat Completions via sibling tool name, but lacks explicit 'do not use when' guidance.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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