generate_image_openai

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

With no annotations provided, the description carries the behavioral disclosure burden. It discloses key behaviors: only absolute output directories are accepted, relative paths rejected, the file naming pattern, and writes confined to the absolute_output_directory. However, it doesn't describe what happens on failure, whether generations are synchronous, API cost implications, or the return value/format. For a generation tool handling disk writes and API calls, some richer operational detail would help.

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

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

The description is well-organized with a clear summary paragraph followed by a bulleted parameter guide. The front-loaded purpose sentence is strong. The parameter notes are genuinely useful (size constraints, quality enums per model, background constraints). It's slightly verbose but every sentence earns its place, given the complex cross-parameter constraints.

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

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

This is a complex tool: 7 params, model-specific constraints, disk-writing behavior, and nested images array. The description and rich schema (100% coverage) together cover the input side well. No output schema exists, so the description should note return behavior, which it does partially ('written to output directory', file naming). It doesn't state whether it returns success status, generated URLs, or file paths, which is a minor gap for API-integration workflows.

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

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

Schema description coverage is 100%, so the baseline is 3 even with minimal description-level parameter info. The description does add helpful cross-parameter constraints (e.g., transparent background requires png/webp, gpt-image-2 doesn't support transparent, quality varies by model family) that go beyond individual parameter schemas. These relational constraints add value, though the schema already documents most per-parameter details thoroughly.

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

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

The description opens with a specific verb+resource: 'Generate one or more images with OpenAI's Image API and save them to disk.' It clearly states the tool's function and scope (website/app assets), and distinguishes it from the sibling generate_image_gemini by explicitly naming 'OpenAI's Image API'. The model list in the description further differentiates it from the Gemini sibling.

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

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

The description gives explicit usage context: 'Use this when the user (or your plan) needs OpenAI-generated assets for a website or app.' This clearly signals the when-to-use scenario and implicitly distinguishes from the Gemini alternative. While it doesn't name generate_image_gemini explicitly as an exclusion, the OpenAI-specific framing makes the selection obvious.

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