llm_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 does disclose two important behaviors: the image is saved locally and the tool returns an embeddable file path. It does not mention authentication requirements, what happens when no provider is specified, rate limits, or failure behavior.

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 two compact sentences. The main action is front-loaded in the first sentence, the provider examples are parenthetical and useful, and the second sentence conveys the output expectation without 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?

The description covers the core contract: generate, save locally, return an embeddable path. However, with six parameters, no output schema, and no annotations, it omits operational details such as how the provider is resolved when only prompt is given, whether endpoint_url is required, and the concrete format of the returned file path.

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%, so the baseline of 3 applies. The tool description adds no parameter-level detail beyond what the schema already provides; for example, endpoint_url is already documented in the schema as an 'Ad-hoc /v1 base URL supporting /v1/images/generations.'

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 opens with a specific action and resource: 'Generate an image' via 'ANY external /v1 endpoint', then lists concrete examples such as SiliconFlow, Together AI, and DALL-E. It also states the outcome ('saves the generated image locally and returns an embeddable file path'), which clearly differentiates it from siblings like llm_generate_video.

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 implies when to use the tool: whenever you need to generate an image through a supported external /v1 endpoint. However, it does not explicitly state when not to use it, nor does it point at alternatives such as llm_generate_video for video generation or other non-generation tools.

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