image_generate

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

Without annotations, the description carries full burden for behavioral disclosure. It only states the generation capability but omits critical traits like execution time, rate limits, content policies, cost implications, or return format. Users cannot anticipate side effects or constraints.

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 a single concise sentence that communicates the core functionality without any extraneous words. Every word earns its place.

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?

For an image generation tool with no output schema and no annotations, the description is too sparse. It lacks information about return values (e.g., image URL, bytes), error handling, or model specificity beyond the name. More behavioral context is needed for safe and effective use.

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?

Since schema description coverage is 100%, the baseline is 3. The description mentions 'text prompts' which corresponds to the prompt parameter, but adds no new meaning or constraints beyond the schema. It does not elaborate on n or size semantics.

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's function: generating images from text prompts using the OpenDalle model. It specifies the verb (Generate), resource (images), and method (from text prompts, using OpenDalle). This clearly distinguishes it from sibling tools like llm_vision (which analyzes images) and other non-image tools.

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?

No guidance is provided on when to use this tool versus alternatives. Siblings include llm_vision for vision tasks, but no differentiation is offered. There is no mention of prerequisites, limitations, or when not to use.

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