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Decode barcode/QR code

decode_image
Read-only

Decode barcodes and QR codes from a base64-encoded image. Returns all codes found, supports QR, EAN, UPC, Code 128, and more, even from noisy photos.

Instructions

Decode barcodes and QR codes from a base64-encoded image. Returns all codes found. Supports QR, EAN-13, EAN-8, UPC-A, UPC-E, Code 128, Code 39, Code 93, ITF, Codabar, and PDF 417. Handles noisy real-world photos by trying multiple preprocessing approaches.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
imageYesBase64-encoded image data (PNG, JPEG, etc.)
format_hintNoExpected barcode format. If omitted, all formats are tried.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
codesYesAll barcodes/QR codes found in the image
Behavior4/5

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

Annotations already include readOnlyHint=true, and the description adds useful context: it returns all codes found and handles noisy photos via multiple preprocessing approaches. This goes beyond the annotation by disclosing output behavior and robustness, though it does not detail error handling.

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 sentences, front-loaded with the primary action, and every sentence adds value. The format list is compact and directly useful, with no fluff or repetition.

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?

The description fully covers what the tool does, what inputs it expects, what outputs to expect (all codes found), and what formats are supported. With an output schema present and readOnlyHint annotation, no further context is needed for correct selection and invocation.

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 parameters are already well-documented. The description mentions the list of supported formats, which overlaps with the format_hint enum, adding little beyond the schema. Baseline 3 is appropriate.

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 verb ('Decode'), the resource ('barcodes and QR codes from a base64-encoded image'), and distinguishes it from siblings (generate_qr, generate_barcode) by focusing on decoding rather than generation. It lists supported formats, making the scope explicit.

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

Usage Guidelines3/5

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

The description implies usage (decode an image to extract codes) but does not explicitly state when to use this tool versus alternatives or mention any exclusions. The contrast with generation siblings is implicit, but no direct guidance is provided.

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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