Brimload 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 · MCP 2025-11-25
- URL
TDQS
Scored across 2 tools
pack_boxes is the single functional tool and send_feedback is clearly a meta/feedback channel; there is no plausible way to confuse the two since their purposes and argument shapes are entirely distinct.
Both tools follow a clean verb_noun snake_case pattern (pack_boxes, send_feedback), so naming is fully predictable despite the small set.
Two tools for a specialized calculator is borderline thin: one core computation tool plus one auxiliary feedback tool leaves little room for adjacent operations, though the narrow domain makes this defensible rather than a mismatch.
pack_boxes covers a lot of ground (max fit, partial loads, weight limits, presets, 3D render link), but there is no tool to enumerate container presets or to plan across multiple containers, which are minor gaps an agent can partly work around via the description's alias list.
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 T1 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: Kept for compatibility, no effect: boxes always rest on
the floor.
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 |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Beyond the read-only/idempotent annotations, it discloses non-obvious behavior: boxes mix orientations across regions, the plain maximum uses the exact T1 solver, `container` overrides `space_*`, `to_bottom` is a no-op because boxes always rest on the floor, and inline images only embed on certain clients while `image_url` is always returned.
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?
Long, but justified by 14 parameters, and the purpose plus the container/space rule is front-loaded. There is minor redundancy between the `include_image` prose and the Returns paragraph about `image_url`, but every section otherwise earns its place.
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?
With no output schema, the description fully specifies the return payload (max fit, loaded count, fill rate, region/block decomposition, weight limit, `image_url`, optional inline image) and all input modes. An agent has everything needed to call and interpret it.
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 0% with 14 parameters, so the description carries the full burden and does: it specifies millimetre units, enumerates container presets and aliases, documents defaults (count=0 loads max), anchor semantics, and weight units.
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?
States a specific verb and resource ('Calculate how many identical boxes fit in a rectangular space, and where') and immediately clarifies the domain (container/pallet/box). It is trivially distinguishable from the sole sibling, send_feedback.
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?
Gives clear conditional guidance: supply EITHER a `container` preset OR explicit space dims, and use `include_image` ONLY when the user wants to see the plan. It handles the key decision points. It does not name alternative tools or state when not to call it, but no relevant alternative exists here.
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 |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations indicate non-read-only (readOnlyHint: false) and non-destructive (destructiveHint: false). The description adds that feedback reaches the Zhihuo team and includes optional contact/category. No contradictions.
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?
Description is well-structured with a brief intro, usage paragraph, and Args. It's front-loaded with purpose, but slightly verbose with the Args section. Still concise enough.
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?
With an output schema present, the description succinctly notes it returns a short confirmation string. All three parameters are explained, and usage guidance is provided. No gaps for a feedback tool.
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 0%, but the description's Args section explains each parameter: message as plain text, contact as optional email/handle, category as optional tag with specific values. This adds essential meaning 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 explicitly states it sends a question, suggestion, or bug report to maintainers. It uses specific verbs and resource, and clearly distinguishes from the sibling tool 'pack_boxes' which deals with 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 lists clear use cases: user questions, improvement suggestions, bug reports. It doesn't explicitly state when not to use, but the list is comprehensive and no alternative tools are needed.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Tool Schema Changelog
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
2 tool updates
- First observed
pack_boxes - First observed
send_feedback
Related MCP Connectors
How many identical cartons fit in a container or space, where they go, plus a 3D loading plan.
Plan optimal container & truck loads: 3D layouts, utilization, centre of gravity, crush checks.
Pack cargo into containers & onto pallets; a verifiable 3D loading plan. Free tier + REST API.
Sea container load calculator. Counts cartons per container and works backwards to order qty.
Related MCP Servers
- AlicenseNot gradedqualityCmaintenancePlan optimal container & truck loads: 3D layouts, right-size the container mix, and check utilization, centre of gravity, crush protection and securing across 200+ equipment types.9 npmMIT
- AlicenseAqualityBmaintenanceEnables AI assistants to plan container and truck loads from plain-English shipment descriptions, returning fitted containers, utilization, non-fitting items, and interactive 3D load plans.151 npmMIT
- AlicenseAqualityDmaintenanceEnables AI agents to pack trucks and shipping containers by generating validated 3D load plans with metrics like LDM, linear feet, and pallet positions through the Hansatic packing API.37 npmMIT

@kubova/mcpofficial
AlicenseAqualityDmaintenanceLets AI agents pack shipping containers using the Kubova calculator, with tools for container loading and API key verification.49 npmMIT
Glama MCP Gateway
Add one secure layer between your agents and this server.