text_to_image

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

With no annotations provided, the description carries the full burden. It discloses the tool's behavior: generating images, saving to disk, and returning a dict. However, it does not mention authentication requirements, rate limits, or potential side effects beyond file saving.

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-structured: a one-line purpose, bullet points for model options, an Args section with parameter details, and a Returns section. It is front-loaded with the core action and remains concise without unnecessary repetition.

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?

The description covers all parameters, model choices, and return format. It lacks error handling information and prerequisites (e.g., API key setup) but is otherwise sufficient for an agent to invoke the tool correctly. The presence of an output schema makes the return description less critical.

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 input schema has 0% description coverage, so the description fully compensates. Each parameter (prompt, model, size, n, images, output_dir, return_mode) is clearly explained with constraints, defaults, and allowed values (e.g., n=1-4). This adds significant value beyond the raw schema.

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 the tool generates images from text using Agnes AI. The verb 'generate' and resource 'images from text' are specific. Sibling tools like image_to_image and text_to_video are differentiated by name and context, making the purpose unambiguous.

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 guidance on model selection, recommending agnes-image-2.1-flash for enhanced quality. However, it does not explicitly state when to use this tool over siblings like image_to_image or text_to_video, although the tool name suffices for basic differentiation.

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