Click on "Install Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type @ followed by the MCP server name and your instructions, e.g., "@3dpacking-mcp Pack 50 boxes of 60x40x30 cm into a 20ft container"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
Container and truck load planning for AI assistants. Describe a shipment in plain English; get back the containers it fits in, how full each one is, what did not fit, and a link to an interactive 3D load plan.
Backed by the 3DPACK.ING solver — the same one behind the web planner. Asked "will 500 cartons fit in a 40-foot", an assistant without this will do arithmetic on volumes, which ignores stacking rules, orientation and weight limits and overstates what fits by a wide margin on real cargo.
The server runs at https://3dpack.ing/mcp over streamable HTTP. Nothing to install,
and it answers on the first call without an account.
Claude Code:
claude mcp add --transport http 3dpacking https://3dpack.ing/mcpAny client that takes a URL — add https://3dpack.ing/mcp. To use your own key
instead of the shared demo account, pass it in the query string:
https://3dpack.ing/mcp?apiKey=your-key&username=your-usernameor send X-3dpacking-Api-Key and X-3dpacking-Username headers, if you would rather
not put a key in a URL. Both are needed together — the API rejects a key without the
account it belongs to.
Claude Desktop — add to claude_desktop_config.json:
{
"mcpServers": {
"3dpacking": {
"command": "npx",
"args": ["-y", "@3dpacking/mcp-server"]
}
}
}Claude Code:
claude mcp add 3dpacking -- npx -y @3dpacking/mcp-serverIt works immediately, with no account — the first call runs against a shared demo account on the free plan. For your own limits, sign up at https://3dpack.ing/login and add:
{
"mcpServers": {
"3dpacking": {
"command": "npx",
"args": ["-y", "@3dpacking/mcp-server"],
"env": {
"THREEDPACKING_API_KEY": "your-key",
"THREEDPACKING_USERNAME": "your-username"
}
}
}
}Both must be set together — the API rejects a key without a username.
Related MCP server: @kubova/mcp
pack_shipment — one required argument, prompt, describing the cargo in plain
English, and an optional speed of fast, normal or thorough.
Things it understands:
Pack 50 boxes of 60x40x30 cm into a 20ft container
Load 100 fragile items 80x60x40cm, max stack 3, into a 40ft high cube
Ship mixed pallets: 10x euro pallets, 15x US pallets, best container mix
Ship 24 pcs 200.3x120.2x100.2 cm (non-tiltable), optimal mix of 40ft and 20ftWhat comes back:
Everything fits: 1 container.
Container 1 (589.3 x 235 x 239 cm internal)
items: 50
of which: 50x Boxes
used: volume 10.9%
5 empty gaps left
Interactive 3D load plan: https://3dpack.ing/app?g=6c4b9488-...The API returns the geometry of every unfilled cuboid in every container, with coordinates. That is what the 3D viewer needs and it is most of the payload on a real shipment. Spending a model's context on the coordinates of empty space, when the link opens a picture of exactly that, is a poor trade — so the summary keeps the count of gaps and drops the geometry. On a small pack that is 791 bytes down to 341; on a large one it is the difference between a usable answer and a flooded context.
A plan limit is not reported as an error. The API returns HTTP 500 for
NotSubscribed, the same status it uses for a solver crash; left alone, an assistant
reads that as "the service is broken" and tells the user to try again later, when what
actually happened is that they asked for a Pro feature. This server sorts the two
apart and relays the offer.
Anything that answers with non-JSON — a proxy, a VPN, a corporate egress allowlist — is reported as a network problem naming what actually replied, rather than as a packing failure. Only one of those is something the user can fix.
Variable | Purpose |
| Your API key. Falls back to the shared demo account. |
| The account the key belongs to. Required with the key. |
| Override the endpoint — staging, self-hosted, or a stub. |
npm install
npm run smoke # calls the live solver with demo credentials and checks the shapesmoke.js is deliberately not mocked for the live checks. The thing that will break
this package is 3dpack.ing changing the shape of its answer, and no amount of mocking
catches that. Run it before publishing. To exercise the success path without spending
a calculation, point THREEDPACKING_ENDPOINT at a stub.
MIT.
Work out how a shipment fits into shipping containers, trucks or pallets, using a real 3D bin-packing solver. Describe the cargo in plain English -- quantities, dimensions, weights, and any constraints such as fragile, non-tiltable, max stack height or a preferred container type -- and get back which containers are needed, how full each one is, anything that did not fit, and a link to an interactive 3D load plan.
Use this instead of estimating from volume. Volume arithmetic ignores stacking rules, orientation and weight limits, and overstates what fits by a wide margin on real cargo.
| Name | Required | Description | Default |
|---|---|---|---|
| speed | No | How hard the solver should look for a better arrangement. Omit to let it choose. Use 'fast' for a quick feasibility check, 'thorough' when the packing quality matters. | |
| prompt | Yes | The shipment, in plain English. Include quantities, dimensions with units, and weights if known. Examples: "Pack 50 boxes of 60x40x30 cm into a 20ft container"; "Load 100 fragile items 80x60x40cm, max stack 3, into a 40ft high cube"; "Ship mixed pallets: 10x euro pallets, 15x US pallets, best container mix". Truncated at 4000 characters. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full responsibility. It discloses the tool uses a solver, returns container needs, fullness, non-fitting items, and an interactive 3D plan. It also warns about volume overestimation, providing valuable behavioral context without contradicting any 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 concise: two paragraphs, each serving a distinct purpose. The first explains functionality and output, the second gives usage guidance. Every sentence adds value, with no redundancy or fluff.
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?
No output schema is present, but the description explicitly lists returned information (containers needed, fullness, non-fits, link to 3D plan). It also covers input requirements and constraints, making it contextually complete for a tool of this complexity.
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 coverage is 100%, so the baseline is 3. The description adds value by explaining how to phrase the 'prompt' parameter (quantities, dimensions, weights, constraints) and provides examples. However, the 'speed' parameter is only described in the schema, not in the main description, so it doesn't fully enhance that parameter's semantics.
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 function: 'Work out how a shipment fits into shipping containers, trucks or pallets' using a 'real 3D bin-packing solver'. It specifies the resource (shipment into containers/trucks/pallets) and the action (work out), making it distinct from generic estimation tools.
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?
Explicitly tells when to use this tool: 'Use this instead of estimating from volume', and contrasts with volume arithmetic. It also provides guidance on input description with examples and constraints, making the usage context clear.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Recent tool additions, removals, and schema changes observed during successful MCP inspections. Dates show when Glama detected each change.
v0.1.0pack_shipmentWith only one tool, there is no possibility of confusion or overlap. The single tool's purpose is clearly defined in its description.
The tool name 'pack_shipment' follows a clear verb_noun pattern, which is a good convention. However, with only one tool, there is no broader pattern to assess for consistency.
A single tool for a domain like 3D packing feels thin, as users might expect additional capabilities such as container management or multi-shipment optimization. The count is at the low end of the borderline range.
The tool covers the core packing operation comprehensively, but the server lacks auxiliary tools for activities like validating container specifications or comparing alternative packing strategies. This leaves some gaps in the overall workflow.
Unclaimed servers have limited discoverability.
If you are the server author, to access and configure the admin panel.
Pack cargo into containers & onto pallets; a verifiable 3D loading plan. Free tier + REST API.
Plan optimal container & truck loads: 3D layouts, utilization, centre of gravity, crush checks.
How many identical cartons fit in a container or space, where they go, plus a 3D loading plan.
Calculate shipment volume (CBM/CFT), weight, and how cargo fits standard shipping containers.
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