ocr_screen

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

Annotations already mark the tool as readOnlyHint=true, idempotentHint=true, and destructiveHint=false. The description adds value by specifying the return format (matched text, bounding box, confidence), which goes beyond annotations. It does not describe failure modes or performance, but the core behavior is clear.

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 two sentences long, front-loads the purpose, and contains no unnecessary words. Every sentence adds value.

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 that the tool has an output schema (covering return values), the description is mostly complete for a simple OCR tool. However, the lack of parameter explanations and the absence of details on language support or failure cases mean it falls short of being fully comprehensive.

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 description coverage is 0%, so the description must explain parameters. It does not mention 'device_id' or 'min_confidence' at all. The description only implies that confidence is returned, but does not explain how the min_confidence parameter affects filtering. This is a significant gap.

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 performs OCR on the current screen, specifies the target use case (no usable accessibility tree), and lists return values (text, bounding box, confidence). This differentiates it from sibling tools like ocr_tap and dump_ui.

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 tells when to use the tool ('for UIs with no usable accessibility tree'), providing explicit context. However, it does not explicitly mention when not to use it or name alternative tools, leaving a minor gap.

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