phone_ocr

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

Beyond the lack of annotations, it provides crucial behavioral context by labeling the tool as 'EXPERIMENTAL' and detailing the execution environment requirements. This manages user expectations regarding stability and setup.

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 definition is concise and effective, front-loading the experimental status and environment requirements before stating the core function.

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 no configurable parameters, detailing the environment and operational constraints (Mirroring, Python version) makes the description maximally complete.

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 has zero parameters, meaning the baseline for this dimension is high. The description adds no semantic meaning to non-existent parameters but is entirely appropriate for a zero-parameter tool.

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 defines the tool's function as OCR of the iPhone screen into text with coordinates. This differentiates it from other screen-based interaction siblings like phone_screenshot or phone_type.

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 explicitly outlines critical environmental prerequisites necessary for the tool to function, namely needing iPhone Mirroring and a specific Python setup (python3 with pyobjc).

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