vision_ocr

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 states the tool extracts all text from images using a vision model, which implies a non-destructive read operation. However, it does not disclose details like whether the model has limitations on image size, supported formats, or rate limits, making the transparency adequate but not thorough.

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 short and front-loaded with the main purpose. The Args and Returns sections are structured clearly. It is reasonably concise, though the Returns line adds minimal value since an output schema exists.

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 has only 1 parameter and an output schema, the description covers the key input semantics and purpose. The sibling context adds differentiation. However, it lacks detail on output format (e.g., concatenated or structured) and potential limitations, but the output schema likely covers return structure.

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 description adds some meaning by explaining images are 'Image references (http(s) URL, local file path, or data URI)', which clarifies the type beyond the schema's simple 'string' specification. With schema description coverage at 0% and only 1 parameter, the description compensates partially but could provide more detail like format expectations or 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 the tool extracts all text from images via a vision model. The verb 'Extract' and resource 'images' with OCR via vision model provides a specific purpose, and it distinguishes itself from sibling tools like vision_analyze (likely for analysis not OCR) and vision_video (for 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 lists accepted image types (URL, path, or data URI) which gives context for when to use, but it does not explicitly say when not to use this tool versus alternatives like vision_analyze or vision_document. The guidelines are implied but lack explicit exclusions or comparisons.

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