generate-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 states that HF_TOKEN is required, which is important auth context, but does not disclose rate limits, output format, latency, or whether the operation is read-only or has side effects. For a generation tool with zero annotation coverage, this leaves significant behavioral 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?

Two sentences, front-loaded with the core action and implementation, followed by the auth prerequisite. No wasted words, though it is somewhat terse.

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?

An output schema exists, so return values need not be explained. However, for a generation tool with no annotations, the description should disclose more about side effects, output content, or constraints. It covers auth but leaves several behavioral aspects unaddressed.

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 coverage is 100%, so the schema already documents both parameters (prompt and num_inference_steps) fully, including ranges and defaults. The description adds no parameter-specific detail beyond what the schema provides; baseline 3 is appropriate.

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 states a specific verb and resource ('텍스트 프롬프트에서 이미지를 생성합니다') and names the underlying implementation (HuggingFace Inference API, FLUX.1-schnell). It clearly distinguishes the tool from siblings like greet, geocode, and calculator, though those siblings are unrelated utilities so differentiation is trivial.

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?

It mentions a prerequisite (HF_TOKEN environment variable) which is useful context, but offers no explicit when-to-use or when-not-to-use guidance or alternatives. Usage is implied by the purpose but not elaborated.

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