understand_image

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

No annotations provided, so description carries full burden. Discloses use of external API, supports multiple image inputs, has size limits (50MB) and format support. Includes system prompt and max_tokens details. Slightly less transparent on authentication or rate limits, but adequate for a read-only tool.

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?

Well-structured with warning, usage guidelines, parameter list, returns, and constraints. However, the warning about not using if same model is repeated, slightly reducing conciseness. Still front-loaded and clear.

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 7 parameters, no schema descriptions, no annotations, and presence of output schema, the description is comprehensive. Covers all parameters, use cases, constraints, and return value. Handles complexity well.

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 0%, but description explicitly explains each parameter's purpose and gives examples (prompt, image_url, etc.). Adds meaning beyond schema, like supported formats and size limits.

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 it calls a multimodal model to understand images, specifying use cases like analysis, description, OCR, and recognition. It distinguishes from sibling tools (audio and video) by focusing on images.

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?

Explicitly says when to use (analyze images) and when not to (code/text files, use Read tool). Also warns if the model is the same as the underlying model, don't call it, providing clear alternatives.

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