Generate a single barcode as a PNG image (Code 128, QR, DataMatrix, EAN-13, UPC-A, PDF417 and more). For a full label with text and layout, write ZPL and use zpl_preview instead.
| Name | Required | Description | Default |
|---|---|---|---|
| data | Yes | Data to encode | |
| type | No | Symbology (code128, qr, datamatrix, ean13, upca, pdf417...) | code128 |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the behavioral transparency burden itself. It discloses that output is a single PNG image, that several symbologies are supported, and that it is not a full-label tool. However, it does not mention error behavior, data validation rules, output delivery details, or size/DPI behavior, which would be useful for a tool with no output schema.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Two short sentences, no filler. The primary purpose is front-loaded, supported symbologies are compactly listed, and the alternative tool is mentioned in a single useful second sentence.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple two-parameter tool with no output schema, the description is mostly complete: it states the output format, scope, supported types, and the main alternative. Slightly missing are details about how the PNG is returned or what error cases look like, but these are minor for this tool's simplicity.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the input schema already documents both `data` and `type`. The description repeats the symbology list but adds no deeper parameter semantics such as constraints, format requirements, or case sensitivity. Baseline 3 is appropriate because the schema carries the parameter-documentation load.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description states a specific verb and resource: generate a single barcode PNG image, and lists the supported symbologies. It also distinguishes itself from zpl_preview by clarifying that zpl_preview is for full labels with text and layout. This leaves no ambiguity about what the tool produces.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description explicitly tells the agent when to choose zpl_preview instead: when a full label with text and layout is needed. It does not exhaustively compare with every sibling such as barcode_verify or cpcl/epl/tspl previews, but it gives the primary routing guidance clearly.
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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Tools are largely organized by language and action, with clear pairs like cpcl_preview/cpcl_validate and zpl_preview/zpl_validate. The ZPL analysis tools (validate, explain, compatibility, command_help) have distinct purposes, though zpl_validate and explain_zpl overlap enough to cause occasional misselection.
Most tools follow a readable {domain}_{action} pattern such as zpl_preview, bulk_submit, and template_list, but there are several deviations: verb-first names like explain_zpl and convert_zpl_dpi, plus noun phrases like zpl_command_help and barcode_png. The mixed conventions are still understandable.
At 21 tools, this sits in the 16-25 'heavy' range, above the ideal 3-15 scope. The count is defensible given four label languages plus barcode, template, bulk, and conversion workflows, but it still feels dense for an agent to navigate.
The surface covers ZPL generation, validation, preview, compatibility, and conversion, plus validation/preview for CPCL, EPL, and TSPL, along with barcode, template, bulk, and language detection features. Minor gaps exist, such as no bulk job cancellation and no compatibility/health tools for non-ZPL languages, but core workflows have no dead ends.