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Kubova-com

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by Kubova-com

generate_report

Generate a CAD-style PDF and 3D PNG report for a container packing job. Use a prior pack result to keep the report consistent with the plan already shown.

Instructions

Generate a CAD-style PDF + 3D PNG report for a packing job. IMPORTANT: to keep the report consistent with a plan you already produced, pass the result object from your previous pack_containers call — the report then renders that exact plan instead of recomputing (which can differ run-to-run). If result is omitted, it packs from cargos + containers. Returns base64-encoded PDF + PNG per container plus the pack result.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
cargosYes
containerNoSingle container to pack into. Use this OR containers[].
containersNoMultiple container types — Kubova picks the best mix.
optionsNo
resultNoOPTIONAL but strongly recommended: the exact `result` object returned by a prior pack_containers call. Pass it here so the report renders THAT plan instead of repacking — this guarantees the PDF matches the packing you already showed the user. Omit only if you have not packed yet.
Behavior4/5

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

Given no annotations, the description properly discloses that omitting result causes repacking (which may differ), and describes the output format. However, it does not mention potential side effects or constraints like rate limits, though none are expected for a report generator.

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 concise with three well-structured sentences: purpose, critical usage guideline, and output summary. Every sentence provides necessary information without redundancy.

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

Completeness4/5

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

For a moderately complex tool with nested parameters and no output schema, the description covers purpose, key usage nuance, and return format. It is adequate but could be enhanced by explaining 'CAD-style' or detailing the options parameter.

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?

The description adds significant meaning only for the result parameter, explaining its role in ensuring consistency. For other parameters (cargos, containers, options), it adds no value beyond the schema, which has 60% coverage, so the description does not fully compensate.

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 tool generates a 'CAD-style PDF + 3D PNG report for a packing job'. It specifies the output format and the resource, effectively distinguishing it from sibling tools like estimate_capacity and pack_containers.

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

Usage Guidelines5/5

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

Explicitly explains when to use the result parameter and why, including the alternative behavior if omitted. This guides the agent to maintain consistency with prior packing plans, which is a critical usage nuance.

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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