ocr_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 does disclose the output structure (text + bbox with both pixel and normalized coordinates), which is useful. However, it does not mention input constraints, error behavior, or any side effects, leaving gaps in behavioral disclosure.

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 a single, succinct sentence that immediately states the core function and return format. No wasted words.

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 tool is simple, but the description lacks guidance on prerequisites or image format. It does describe the return output, which compensates for the missing output schema. However, it doesn't cover usage context or when alternatives should be chosen, making it merely adequate.

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 input schema has 50% description coverage, but the tool description adds minimal meaning for the parameters. It identifies the image as the input via context, but does not explain the language parameter or clarify how images are passed (path, URL, base64). The language parameter is only described in the schema, not reinforced.

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 performs OCR to extract all text blocks from an image and return text plus bounding boxes. The term 'OCR' and the focus on 'text blocks' distinguishes it from sibling image tools like describe_image or locate_object.

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 provides no explicit guidance on when to use this tool versus other vision tools. It neither mentions alternatives nor specifies exclusions. The context of text extraction is implied by the name, but no direct comparison is given.

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