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auto_enhance

Automatically tries up to seven image enhancement strategies to decode a QR code from a single image. Use after a failed decode to get a retryable result without manual parameter selection.

Instructions

Automatically try enhancement strategies to decode a QR code in one call.Tries up to 7 strategies (upscale, sharpen, contrast, denoise, combos) in sequence — returns as soon as one succeeds.Ideal for RETRYABLE results from decode_qrcode_full:no manual bbox estimation or operation selection needed.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
bboxNoOptional target region [x, y, width, height].If omitted, processes the entire image.
image_urlNoPublic image URL.
image_pathNoLocal image absolute path — preferred when available.
image_base64NoBase64-encoded image.
Behavior4/5

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

With no annotations, the description carries the behavioral transparency burden. It discloses the key trait: 'Tries up to 7 strategies (upscale, sharpen, contrast, denoise, combos) in sequence — returns as soon as one succeeds.' This is concrete behavioral description, though it omits failure behavior and any safety implications.

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 two efficient sentences, front-loaded with the core action. Every part adds value: the strategy count, sequence behavior, and ideal use case. No wasted words.

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

Completeness3/5

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

The tool is moderately complex, with no output schema and no annotations. The description explains the strategy sequence but does not clarify the return value format or the fact that an image source is required despite the schema listing zero required parameters. These are notable gaps for a one-call tool.

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 description coverage is 100%, so the schema fully documents each parameter. The description adds no additional parameter semantics, and it does not clarify that at least one image source (URL, path, or base64) is required despite all being marked optional in 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?

The description clearly states a specific verb and resource: 'Automatically try enhancement strategies to decode a QR code in one call.' It distinguishes from siblings by noting it tries up to 7 strategies and requires 'no manual bbox estimation or operation selection needed,' which positions it as an automatic alternative.

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 gives clear when-to-use context: 'Ideal for RETRYABLE results from decode_qrcode_full' and 'no manual bbox estimation or operation selection needed.' It implies when not to use (when manual control is desired) but does not explicitly name an alternative like enhance_and_decode.

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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