get_screenshot

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

No annotations provided, but description fully discloses downscaling behavior, scale factor, coordinate multiplication advice, and distinction from UI tree coordinates. This is rich behavioral context beyond what the schema provides.

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?

Description is fairly long but all sentences are informative. No wasted words, but could be slightly more concise. Still clear and well-structured.

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 low complexity (1 param, no output schema), description covers essential behavior, usage context, and coordinate scaling. It mentions response includes scale factor, but does not fully specify the response structure (e.g., JSON format). This minor gap prevents a 5.

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?

Only one parameter 'device_id' with schema description 'Device ID'. Description does not add further semantics (e.g., how to obtain ID). Schema coverage is 100%, so baseline 3 is appropriate.

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 verb (Capture), resource (screenshot), and distinguishes from the sibling tool 'save_screenshot' by specifying low-quality for LLM analysis versus full-quality.

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?

Explicitly says when to use (for AI/LLM processing) and when not to (use save_screenshot for full-quality), providing clear alternative.

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