Status of one bulk job you own: state, attempt count and — when finished — the summary with produced count and per-row failures (row number + reason; failed rows never block the rest). Finished outputs stay downloadable for 7 days via the REST endpoint the response names.
| Name | Required | Description | Default |
|---|---|---|---|
| job_id | Yes | Job id from bulk_submit |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden, and it does this well. It discloses ownership constraints, state and attempt count, summary contents, per-row failure details, the non-blocking nature of failed rows, and a 7-day retention window via a named REST endpoint.
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?
The description is dense but readable, front-loading the core purpose before adding behavioral detail. Every clause adds meaningful information, and it avoids repeating schema or sibling names unnecessarily.
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 and no annotations, the description explains both what is returned and important behavioral caveats. It covers access scope, timing, failure handling, and retention, so an agent has enough context to call it correctly.
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%: job_id is already documented as 'Job id from bulk_submit'. The description adds the ownership nuance ('you own') but does not need to elaborate further for a single well-defined parameter.
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 clearly identifies the operation as returning status for a single bulk job the caller owns, enumerating specific fields (state, attempt count, summary, failures). This distinguishes it from siblings like bulk_submit or bulk_list by specifying 'one bulk job' rather than submission or listing.
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?
It gives clear context: use this when you have a specific job_id from bulk_submit and need status for that one owned job. It does not explicitly name alternatives or say when not to use it, but the single-job scoping and ownership condition are strong enough guidance.
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.