image_generate

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

With no annotations, the description carries the full burden of behavioral disclosure. It explains defaults (gpt-image-2, quality), constraints (size multiples of 8, n 1-4), and automatic model switching for ultra-large sizes. However, it does not disclose whether the tool saves files, what the return value looks like, or potential side effects, leaving some behavioral transparency gaps.

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 concise and well-structured, with a one-sentence purpose followed by a bulleted list of argument explanations. Every sentence provides useful information without redundancy or waste.

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?

Given the tool's complexity (6 parameters, 1 required) and lack of annotations, the description covers all parameters, defaults, constraints, and usage recommendations. It also notes output safety. It does not mention explicit error conditions or prerequisites, but the output schema likely covers return values. Overall, it is quite complete for a generation tool.

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?

The schema has zero description coverage, so the description fully compensates by documenting all 6 parameters with detailed semantics: prompt (subject, composition, style, lighting, prohibited content), size (format and multiple-of-8 constraint), model (default and auto-upgrade), n (range and recommendation), quality (default), and output_dir (safety constraint). This is significantly more informative than the schema alone.

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 clearly states '通过 MetaRouter 文生图' (generate images from text via MetaRouter), which is a specific verb+resource. It distinguishes from sibling tools like image_edit, image_batch_edit, and image_multi_reference by focusing on generation rather than editing or reference-based tasks.

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 provides clear context for when to use this tool: it is for text-to-image generation. It includes usage tips such as '高分辨率建议使用 1' and '超大尺寸自动使用 gpt-image-2-pro', which help with parameter choices. However, it does not explicitly mention alternatives or exclusions, such as when to use image_edit instead.

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