vision_crop_verify

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It accurately describes the multi-step process (crop, enlarge, re-analyze) and the intended use case (verifying small text, button states). It does not mention potential failure modes, authentication, or rate limits, but for a read-only analysis tool, the core behavior is transparent and aligns with what the tool does.

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 well-structured: a concise opening sentence explaining the core action, a usage guideline sentence, a clear 'Do NOT use' sentence with alternatives, and a tidy list of parameters. It is appropriately sized with no filler or redundant text, and every sentence serves a purpose.

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 complexity (7 params, no annotations, no schema descriptions), the description covers all essential aspects: what it does, when to use it, what inputs are needed and their formats, and how it relates to sibling tools. The output schema exists, so the description need not detail return values. This is effectively a complete guide for an agent to select and invoke the tool correctly.

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?

The schema description coverage is 0%, so the description must fully compensate. It provides a separate Args section explaining every parameter, including serialization details ('bbox: Crop region [x1, y1, x2, y2] in original image pixel coordinates'), format choices ('image_source: local file path or remote URL... auto-detects format'), and backward-compatibility notes. This adds meaning far beyond the raw schema types.

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: 'Crop a region of an image, enlarge it, and re-analyze with OCR/VLM for verification.' It identifies a specific verb ('crop', 'enlarge', 're-analyze') and a resource (image region) and distinguishes itself from siblings by explicitly naming vision_inspect and vision_diff as alternatives for different tasks.

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?

Provides explicit when-to-use guidance: 'Use this tool ONLY when you need to zoom into and verify a specific region of an image.' It also gives clear exclusions with alternatives: 'Do NOT use for: full image analysis (use vision_inspect), comparing two images (use vision_diff).' This leaves no ambiguity about when to select this tool versus its siblings.

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