Decode barcodes from a photo or scan of a PRINTED label (PNG, JPEG, BMP or GIF) and report what a software decoder reads back. Honest by design: 'read' does not guarantee every handheld scanner will read it, and 'not read' does not condemn the label (photo angle/focus matter) — the response says so.
| Name | Required | Description | Default |
|---|---|---|---|
| image_base64 | Yes | Base64-encoded image bytes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the behavioral burden. It discloses that decoded results are honest but not authoritative: a 'read' does not guarantee a physical scanner will read it, and a 'not read' does not condemn the label. It could add more detail about error handling or exact response shape, but it covers the key caveat.
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 purposeful sentences, front-loaded with the action and input constraints, followed by an important caveat. There is no filler or redundant restatement of the tool name.
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 one-parameter tool with no output schema, the description adequately covers input formats, purpose, and the interpretation of results. It only sketches the return shape ('report what a software decoder reads back'), but for this simple tool that is sufficient.
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?
The schema already documents image_base64 as Base64-encoded image bytes with 100% coverage, so the baseline is 3. The description adds meaningful parameter context by requiring photos/scans of printed labels and enumerating supported PNG, JPEG, BMP, and GIF formats, which goes beyond the schema.
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 opens with a concrete action ('Decode') and resource ('barcodes from a photo or scan of a PRINTED label'), and clarifies that it reports what a software decoder reads back. This clearly differentiates it from siblings like barcode_png, which likely generate barcodes rather than verify them.
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 provides clear usage context: use this tool on printed-label photos or scans, and it explicitly lists supported formats (PNG, JPEG, BMP, GIF). It does not explicitly name alternatives or state when not to use it, but the printed-label restriction makes the intended boundary reasonably clear.
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.