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ocr

Read on-screen text from the display or a specific window. Returns recognized text lines with clickable coordinates for clicking or reading, ideal for images, games, or custom-drawn views.

Instructions

Read on-screen TEXT via the platform OCR engine (Apple Vision on macOS, Windows.Media.Ocr on Windows). Captures the display (or window="") and returns the recognized text lines, each with a clickable center in the same pixel space as a screenshot — so you can both READ the text and click a line with left_click(x, y). Returns text only (no image), so it is a cheap, fast way to pull text off the screen. Best when you need to read or click TEXT on a surface where marks come back empty — canvas, games, image-rendered or custom-drawn views — or to grab a lot of text at once without parsing a screenshot. For native/web UI with an Accessibility tree, screenshot(marks=true) + click_mark is still more precise. Languages default to Simplified Chinese + English; pass languages=[...] (BCP-47) to override.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
windowNoCapture only a single on-screen window (case-insensitive substring of its title or app name) instead of the whole display. Smaller, sharper image → better recognition of small text.
languagesNoBCP-47 language hints in priority order (e.g. ["zh-Hans", "en-US"]). Omitted, defaults to Simplified Chinese + English.
Behavior5/5

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

No annotations exist, so description fully discloses behavior: returns text lines with clickable centers, cheap/fast, captures display or window, defaults to zh-Hans+en, and details pixel-space coordinates. No hidden side effects.

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?

Front-loaded with purpose and engine. Every sentence adds unique value; no filler. Structured with use cases and comparisons.

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

Completeness5/5

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

No output schema, but description explains return format (text lines with clickable centers). Low parameter count fully covered. Distinguishes from siblings. Complete for an AI agent to use correctly.

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% with descriptions for both parameters. Description adds context (window substring match, language defaults) and explains why a smaller window improves recognition, enhancing the 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?

Clearly states 'read on-screen TEXT' specifying verb and resource, identifies OCR engine, and distinguishes from screenshot/click tools by emphasizing text-only output.

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

Usage Guidelines5/5

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

Explicitly describes when to use OCR (canvas, games, custom-drawn views) and when to prefer screenshot+click_mark (native/web UI with accessibility tree), providing clear alternatives.

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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