ocr_image

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

With no annotations, the description carries the burden and does disclose meaningful behavior: preserving reading order and paragraph structure, accepting 1–9 images, and returning transcripts in the given order. It does not mention output format or error behavior, but the core read-only transcription behavior is well covered.

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?

Three short sentences with the core action front-loaded. Every sentence earns its place, and there is no redundancy or filler.

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 description covers purpose, input limits, ordering, and typical use cases, which is sufficient for an agent to select and invoke the tool. Since there is no output schema, a brief note on return format would improve completeness, but this is a minor gap for a simple OCR 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%, so the baseline is 3. The description reinforces image order and count, which the schema already documents, but adds no parameter-level meaning for model, language, or provider beyond what the schema provides.

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 states a specific verb ('transcribe all text') and resource ('image(s)'), and adds output characteristics (reading order, paragraph structure). This clearly distinguishes it from sibling tools like analyze_image and describe_image.

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?

It gives concrete use cases (screenshots, scans, invoices, slides) but does not explicitly state when to choose this tool over analyze_image or describe_image, nor when not to use it. The differentiation is implied by the OCR action rather than stated.

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