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retrieveModel

Retrieves a model instance, providing basic information about the model such as the owner and permissioning.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
modelYes
x-hapi-auth-stateNo

TDQS

C2.9/5.0
Behavior3/5

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

No annotations are provided, so the description carries the full burden. It discloses that the return is 'basic information' with examples (owner, permissioning), giving a sense of scope. However, it does not mention authentication needs, error behavior, or explicitly confirm read-only semantics beyond the verb 'retrieves'.

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?

The description is a single, well-structured sentence that focuses on the core purpose and return value. No filler or redundant text, making it easily scannable by an agent.

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

Completeness2/5

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

The tool has no annotations, no output schema, and sparse parameter documentation. The description gives a basic purpose and some return content, but lacks response shape, parameter semantics, and usage context. For reliable invocation, an agent would still have to guess about critical details.

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

Parameters1/5

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

Schema coverage is 0% and the description does not compensate. The 'model' parameter is implied by the purpose but its format (ID vs. name) is not explained. The 'x-hapi-auth-state' parameter is entirely omitted, leaving the agent without any guidance on how to populate it.

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

Purpose4/5

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

The description uses a specific verb ('retrieves') and resource ('model instance'), clearly indicating it fetches a single model's details. It provides extra context about return content (owner, permissioning). It does not explicitly differentiate from siblings like listModels, but the singular 'instance' implies the distinction.

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

Usage Guidelines2/5

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

No guidance is given on when to use this tool versus alternatives such as listModels or deleteModel. There are no prerequisites, exclusions, or alternative tool references. The description only states what the tool does, not when to use it.

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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TDQS

B3.2/5.0
Disambiguation5/5

Each tool targets a distinct resource and action: image creation vs. editing vs. variation, audio transcription vs. translation, moderation, and model management. Even the closely related image tools are clearly differentiated by their function (generate, edit, vary).

Naming Consistency5/5

All tool names follow a consistent camelCase verb_noun pattern: create*, deleteModel, listModels, retrieveModel. The verbs are specific to the action (create, delete, list, retrieve) and the nouns correspond to the target resource.

Tool Count5/5

The 9 tools are well-scoped for the domains they cover: image generation, image manipulation, audio processing, moderation, and model management. Each tool serves a distinct purpose without unnecessary overlap or bloat.

Completeness2/5

The tool set lacks the core OpenAI functionality of text generation (e.g., chat/completions or completions). This is a major gap for a server named 'OpenAI Tools', as agents cannot perform basic language model requests. The image, audio, and moderation tools are reasonably complete, but the overall surface is severely incomplete.