generate-image

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It states the tool generates AI images using a specific model, which implies it's a creation/mutation operation (likely not read-only), but doesn't cover critical aspects like rate limits, authentication needs, cost implications, or output format. For a generative tool with zero annotation coverage, this is a significant gap in transparency.

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 extremely concise and front-loaded: two sentences that directly state the tool's function and model used, with zero wasted words. Every sentence earns its place by providing essential information without redundancy or fluff, making it efficient for quick understanding.

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 complexity of an AI image generation tool with no annotations and no output schema, the description is incomplete. It lacks details on behavioral traits (e.g., mutation nature, rate limits), output expectations (e.g., image format, size), and usage constraints. While it specifies the model, more context is needed for the agent to use this tool effectively and safely in varied scenarios.

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%, with the single parameter 'prompt' fully documented in the schema (type, length constraints, and description recommending English). The description adds no additional parameter semantics beyond what the schema provides, such as prompt formatting tips or model-specific guidelines. With high schema coverage, the baseline score of 3 is appropriate as the description doesn't compensate but also doesn't detract.

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 purpose: '텍스트 프롬프트를 입력받아 AI 이미지를 생성합니다' (takes a text prompt as input to generate an AI image). It specifies the action (generate) and resource (AI image), and mentions the specific model (FLUX.1-schnell). However, it doesn't explicitly differentiate from sibling tools like 'calculator' or 'geocode', though the domain is distinct enough that differentiation is implied rather than explicit.

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 no guidance on when to use this tool versus alternatives. It mentions the model used (FLUX.1-schnell) but doesn't specify contexts, prerequisites, or exclusions. For example, it doesn't indicate if this is for creative vs. technical images, or when to prefer this over other image-generation methods. The lack of usage context leaves the agent without explicit direction.

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