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Byte-Naut

npu-vision-fallback

by Byte-Naut

analyze_screen

Capture a screen region, run NPU UI detection and OCR in parallel, and return an ordered list of interactive elements with their visible text, enabling agents to understand and act on the screen.

Instructions

Capture a screen region, run NPU YOLO UI detection and system OCR in parallel, then spatially fuse the results. Returns an ordered list of interactive elements (buttons, fields, headings, …) each annotated with the visible text inside them — ideal for agents that need to understand and act on the current screen. region=[x1,y1,x2,y2] in screen coords; omit for full screen.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
regionNo[x1, y1, x2, y2]
min_confidenceNoMinimum confidence threshold (default 0.30)
Behavior3/5

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

Discloses parallel execution and fusion, and return format. No annotations provided, so description must cover behavior; it doesn't mention permissions, side effects, or that it's read-only. Adequate but not thorough.

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

Conciseness4/5

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

Two sentences front-loaded with core action and result, then usage details. No wasted words, though could be slightly more compact.

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?

Given no output schema or annotations, description adequately explains what the tool returns (ordered list of interactive elements with visible text). Missing ordering criteria but sufficient for an AI agent.

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

Parameters4/5

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

Schema covers both parameters (100% coverage). Description adds meaning: 'region=[x1,y1,x2,y2] in screen coords; omit for full screen' provides practical nuance beyond schema.

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?

Description clearly states specific action: capture screen, run NPU YOLO UI detection and OCR in parallel, fuse results, return ordered list of interactive elements with text. Distinct from siblings like detect_ui or ocr_region.

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?

Explicitly says 'ideal for agents that need to understand and act on the current screen' and explains region parameter usage. Lacks direct comparison to siblings but provides enough context for appropriate use.

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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