vision_analyze_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 indicates the tool calls a cloud visual large model, implying latency and cost, but does not disclose rate limits, authentication needs, or error handling for invalid inputs. The description adds some context but is not comprehensive.

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 efficiently convey the tool's purpose and usage options without waste. The most important information (image analysis, cloud model, flexible input) is front-loaded.

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 tool's moderate complexity (5 params, no nested objects, no output schema), the description covers the key aspects: what it does, how to use it (prompt/preset), and input formats. It does not describe the return format, but that is a minor omission for a text-generation tool.

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% with all parameters described. The description adds value by explaining that prompt overrides preset, providing a list of preset options, and clarifying the flexible input formats for the image parameter. This goes beyond the schema's individual descriptions.

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 as image understanding using a cloud vision model for tasks like description, OCR, chart/UI/error analysis. The verb 'analyze' and resource 'image' are explicit, and it distinguishes from siblings by focusing on static images rather than video or face detection.

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 mentions using prompt or preset, but does not explicitly state when to use this tool versus alternatives like vision_analyze_video. It implies usage for static images but lacks explicit when-not or alternative guidance.

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