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adb_screencap_annotated

Capture a device screenshot annotated with numbered UI element boxes. Returns a labeled image and text legend for referencing elements by number in LLM workflows.

Instructions

Take a screenshot with UI element bounding boxes and numbered labels composited directly onto the image. Returns the annotated PNG path plus a text legend mapping each element number to its identity. Ideal for LLM workflows that need to reference specific UI elements by number rather than by coordinates.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
clickableOnlyNoOnly annotate clickable/scrollable elements (default true — reduces visual noise)
filenameNoOutput filename (default: annotated_<timestamp>.png)
deviceNoDevice serial
Behavior3/5

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

With no annotations, the description carries full burden. It discloses the compositing of bounding boxes and numbers, and mentions the default parameter behavior (clickableOnly reduces noise). However, it does not mention prerequisites, side effects, or resource usage. Adequate but not comprehensive.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Two sentences: the first describes the action and output, the second gives the use case. Every sentence adds value. No redundant or vague phrasing. Efficiently front-loaded.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Despite lacking an output schema, the description explains the return values (path and legend). The parameters are fully documented in the schema. The tool is simple and the description covers the key aspects, but could mention if the legend is a separate file or included in the response.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, with each parameter well-described in the schema (clickableOnly: boolean filtering clickable/scrollable, filename: output name, device: serial). The description does not add significant new information about the parameters beyond what the schema provides, earning the baseline score.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the action (screenshot with bounding boxes and labels), the resource (UI elements), and the output (annotated PNG path and text legend). It distinguishes this tool from plain screencap by emphasizing numbered labels for LLM workflows.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description explicitly states it is 'Ideal for LLM workflows that need to reference specific UI elements by number rather than by coordinates,' providing clear context. It does not explicitly mention when not to use it or name specific alternatives, but the implication is clear.

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

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