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npu-vision-fallback

npu-vision-fallback

by Byte-Naut
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Python
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detect_ui

Locate and classify UI elements in screen regions using YOLOv8n object detection accelerated by NPU or CPU, outputting labeled bounding boxes with confidence scores.

Instructions

Detect objects / UI elements in a screen region using YOLOv8n on OpenVINO (NPU or CPU). Returns bounding boxes with labels and confidence scores. region=[x1,y1,x2,y2] in screen coords; omit for full screen.

Tool call template

This tool declares no required parameters. Add optional parameters from the input schema as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {},
    "name": "detect_ui"
  }
}

Input Schema

NameRequiredDescriptionDefault
regionNo[x1, y1, x2, y2]
backendNoForce a specific backend (openvino_npu or openvino_cpu)

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.0.0

TDQS

A4.4/5.0
Behavior4/5

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

No annotations are provided, so the description carries the full burden. It discloses the technology stack (YOLOv8n, OpenVINO, NPU or CPU), the output (bounding boxes, labels, confidence scores), and that region is optional (full screen). It does not mention side effects or permissions, but for a read-only detection tool, these are non-critical. The description is transparent enough for agent reasoning.

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?

The description is extremely concise yet informative: two sentences cover purpose, technology, output, and parameter usage. No fluff. The critical information is front-loaded, making it easy for an AI agent to quickly grasp the tool's function.

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, the description adequately explains return values (bounding boxes, labels, confidence). It mentions region usage and backend options. It could optionally describe how to interpret bounding box coordinates or the label set, but for a UI element detection tool, this is sufficient for most use cases.

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 coverage is 100%, so the schema already describes both parameters. The description adds value by explaining the region parameter format with screen coordinates and the 'omit for full screen' usage. It also implies backend choices via 'NPU or CPU'. This goes beyond the schema, earning a 4.

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 tool detects objects/UI elements in a screen region using a specific model (YOLOv8n) and returns bounding boxes with labels and confidence scores. It distinguishes from sibling tools like ocr_region (OCR) and analyze_screen (likely different analysis), making the purpose unambiguous.

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 explains the region parameter format and that omitting it triggers full-screen detection. However, it does not explicitly contrast with sibling tools (e.g., when to use detect_ui vs ocr_region) or provide negative usage guidance. The 'omit for full screen' is helpful, but a brief note on alternatives would improve it.

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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