Zhihuo Container Loading
Server Details
How many identical cartons fit in a container or space, where they go, plus a 3D loading plan.
- Status
- Healthy
- Last Tested
- Transport
- Streamable HTTP
- URL
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Tool Definition Quality
Average 4.5/5 across 2 of 2 tools scored.
The two tools are completely distinct: pack_boxes performs container loading calculations, while send_feedback is for contacting the tool maintainers. There is no overlap.
Both tools follow a clear verb_noun pattern: pack_boxes and send_feedback. The naming is consistent and predictable.
The server has only two tools, which is slightly minimal for a container loading server but still acceptable. The feedback tool is auxiliary, and the core functionality is captured in a single, powerful tool.
The main packing tool is comprehensive, but there is no tool to list available container presets or retrieve their dimensions. Users must rely on the tool's internal knowledge of presets, which could be considered a minor gap.
Available Tools
2 toolspack_boxesARead-onlyIdempotentInspect
Calculate how many identical boxes fit in a rectangular space, and where.
The space is a shipping container, a pallet area, leftover container space,
or a larger box. Give the box's L/W/H and EITHER a `container` preset OR the
space's L/W/H (all in millimetres - container presets are mm). Boxes mix
orientations across regions to pack densely; the plain maximum-load answer
uses the exact SmartPacker T solver.
Args:
box_length: Box length.
box_width: Box width.
box_height: Box height.
container: Container preset instead of explicit space dims (fills the
internal usable L/W/H in mm; overrides space_* when recognised).
ISO dry: 20GP 20HQ 40GP 40HQ 45GP 45HQ; NA domestic: 53HC 48HC;
reefers: 20RF 40RF 40RH; EU pallet-wide: 40HQ-PW 45HQ-PW. Many
aliases (20/40/45, 20ft, 40HC, 40', 40 reefer, ...).
space_length: Space length (front-back). Use this OR `container`.
space_width: Space width (left-right).
space_height: Space height (up).
count: How many boxes to load. 0 (default) loads the maximum.
to_front: Anchor a partial load to the front (else the back).
to_left: Anchor a partial load to the left (else the right).
to_bottom: Anchor a partial load to the bottom (else the top).
box_weight: Weight of one box (optional; same unit as max_weight).
max_weight: Max total load weight (optional; 0 = no weight limit).
include_image: Set true ONLY when the user wants to SEE or visualize
the loading plan / 3D layout (e.g. "show the loading plan",
"visualize it", "what does it look like?"). Default false is
faster and lighter and still returns `image_url`, a shareable
link to the 3D render. When true, the image is ALSO embedded
inline (Claude Desktop and Cherry Studio display it; the
claude.ai web app shows only the link either way).
Returns the maximum that fits, how many were loaded, the fill rate, the
region/block decomposition (each block's position, orientation, and
grid), any weight limit applied, an `image_url` link to the rendered 3D
loading plan, and - when include_image is true - the inline image too.
| Name | Required | Description | Default |
|---|---|---|---|
| count | No | ||
| to_left | No | ||
| to_front | No | ||
| box_width | Yes | ||
| container | No | ||
| to_bottom | No | ||
| box_height | Yes | ||
| box_length | Yes | ||
| box_weight | No | ||
| max_weight | No | ||
| space_width | No | ||
| space_height | No | ||
| space_length | No | ||
| include_image | No |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations mark the tool as readOnlyHint and idempotentHint. The description aligns by stating 'Calculate' and details the output (max load, fill rate, image_url). It adds behavioral context like SmartPacker T solver and orientation mixing, which is valuable beyond annotations.
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 front-loaded with a clear summary, followed by parameter details and return values. It is somewhat long due to listing container aliases, but these are relevant. Overall, it is well-structured and earns its length.
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?
Given the tool's complexity (14 parameters, no output schema, no param descriptions), the description covers all parameters, return values, and special behaviors like include_image and container presets. It is nearly complete; a brief note on error cases would push it to 5.
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 input schema has 0% parameter descriptions, so the description must compensate. It provides detailed explanations for each parameter, including container aliases and include_image behavior. This adds significant meaning beyond the schema's bare titles and types.
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 first sentence clearly states the verb and resource: 'Calculate how many identical boxes fit in a rectangular space, and where.' It distinguishes well from the only sibling tool (send_feedback) by focusing on spatial packing.
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 explains when to use container presets vs explicit space dimensions and when to set include_image=true. It lacks explicit 'when not to use' but covers key decision points. Sibling is unrelated, so no confusion.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
send_feedbackAInspect
Send a question, suggestion, or bug report about this container-loading tool to the Zhihuo maintainers.
Use this when the user wants to ask the tool's authors a question,
suggest an improvement (e.g. another container type or output field),
or report a result that looks wrong. The note reaches the Zhihuo team.
Args:
message: The question, suggestion, or bug report, in plain text.
contact: Optional email or handle if the user would like a reply.
category: Optional tag - "question", "suggestion", "bug", or "other".
Returns a short confirmation string.
| Name | Required | Description | Default |
|---|---|---|---|
| contact | No | ||
| message | Yes | ||
| category | No |
Output Schema
| Name | Required | Description |
|---|---|---|
| result | Yes |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already indicate the tool is not read-only, not destructive, and open-world. The description adds that feedback reaches the Zhihuo team and returns a confirmation string. No contradictions. Additional behavioral context is provided, such as it being a communication channel to maintainers.
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 concise with no wasted words. It front-loads the core purpose, then provides usage context, parameter details, and return value. The structure is logical and easy to parse.
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 feedback tool, the description covers all necessary aspects: what it does, when to use it, all parameters, and the return value. The output schema is mentioned implicitly. No gaps.
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?
Despite 0% schema coverage, the description thoroughly explains each parameter: message (required plain text), contact (optional email/handle), category (optional with suggested values). This adds meaningful semantic information beyond the schema's type-only definitions.
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 states the tool's purpose: 'Send a question, suggestion, or bug report about this container-loading tool to the Zhihuo maintainers.' This distinguishes it from the sibling tool 'pack_boxes', which focuses on packing boxes.
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 when to use the tool: when the user wants to ask a question, suggest an improvement, or report a bug. It does not explicitly exclude other uses but clearly defines its domain. Could be improved by stating when not to use, but adequate for most cases.
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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