A
licenseNot graded
qualityC
maintenancePlan 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.
16
MIT
3DPACK.ING — 3D Bin Packing
by ing.3dpack
Server Details
3D bin packing for freight: cartons and pallets in plain English, the fit and a 3D load plan.
If you are the author of this connector, you can claim ownership with GitHub, an HTTP challenge, or a DNS record. Claimed connector authors can inspect health checks, view analytics, and manage their listing.
- Status
- Healthy
- Last Tested
- Transport
- Streamable HTTP
- URL
- Repository
- OnurGumus/3dpacking-mcp
- GitHub Stars
- 0
- Server Listing
- 3dpacking-mcp
Available Tools
1 toolpack_shipmentPack a shipment into containers or trucksAInspect
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.
For cargo that must not overhang -- drums, glass, anything that must stay level -- set stability. It is the one constraint the prompt cannot express, because it governs how the solver stacks rather than what is being shipped.
ParametersJSON Schema
| 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. | |
| stability | No | How much of a box must rest on what is underneath it, as a percentage of its own footprint. Omit for the standard rule of 75, which allows a quarter of a box to overhang and packs the most. Raise it for cargo that must not lean -- drums, glass, anything top-heavy -- and use 100 when every stacked box has to sit fully supported. A higher value is steadier and fits fewer items, so expect more containers or more left over. |
TDQS
A4.7/5.0
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of disclosing behavior, and it does well: it explains the solver-based nature, the class of outputs (containers needed, fullness, non-fit items, 3D load plan link), and the trade-off that higher stability means fewer items fit. It does not mention potential failure modes, rate limits, or side effects, but for a computation/planning tool the behavioral picture is clear.
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 well-structured and front-loaded: the first sentence states the core action and return value, the second paragraph gives usage guidance, and the third focuses on the subtle stability parameter. Each sentence earns its place; there is no filler or redundant restatement of the tool name.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a tool with no output schema and no annotations, the description provides a remarkably complete picture: how to describe cargo, what outputs to expect, when to prefer this tool over volume estimation, and how to handle the special stability constraint. An agent has enough context to invoke the tool correctly and interpret the results without guessing.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the baseline is 3. The description adds meaningful semantic context beyond the schema, especially for `stability`: it explains why the prompt cannot express this constraint, how the solver uses it, and the trade-off between packing density and support. This goes beyond the schema's plain description and helps an agent craft better inputs.
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 opens with a specific verb and resource: 'Work out how a shipment fits into shipping containers, trucks or pallets, using a real 3D bin-packing solver.' It clearly states the tool's function and the outputs it produces, so an agent immediately knows what the tool does and what it returns. There are no siblings to distinguish from, but the purpose is unambiguous on its own.
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?
The description explicitly says 'Use this instead of estimating from volume' and explains why volume arithmetic is inadequate, which is strong when-to-use guidance. It also provides specific guidance about when to set `stability`, covering an important non-obvious decision point. This fully equips an agent to choose the tool correctly and configure it appropriately.
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. Dates show when Glama detected each change.
1 tool update
- First observed
pack_shipment
Frequently Asked Questions
Claiming proves that you control a remote MCP connector. It does not move, proxy, or interrupt the server.
Open the connector listing, choose Claim ownership, and sign in to Glama.
Complete one verification method:
GitHub identity — fastest for official registry listings. For a namespace such as
io.github.alice/server, link the matching GitHub user, then choose Claim with GitHub. An organization namespace such asio.github.acme/serveralso needs that organization to have installed the Glama AI GitHub App and approved its permissions, because GitHub discloses organization membership only to apps it has installed. Use HTTP or DNS when it has not.HTTP challenge — works when you can deploy a public file. Generate a token, publish the exact JSON Glama shows at
/.well-known/glama.jsonon the same origin as the connector, then choose Check HTTP challenge.DNS challenge — works when you control DNS but cannot change the server. Generate a token, create the exact TXT record Glama shows, wait for it to propagate, then choose Check DNS challenge.
After verification, Glama sends a confirmation email and gives you access to listing details, thumbnails, health checks, and analytics. Keep the HTTP file or DNS record in place: Glama periodically checks it and ownership remains verified while the token is discoverable.
The HTTP ownership file has this structure:
{
"$schema": "https://glama.ai/mcp/schemas/connector.json",
"claim": "glama_claim_..."
}Claim tokens are opaque, stable, and bound to the signed-in Glama account. They contain no email address or other personal information. If Glama can no longer discover a verified HTTP or DNS token, it starts a seven-day grace period before removing claim-based access. Restore the same token during that period to keep ownership verified. Never publish an email address, Glama session token, GitHub token, or connector credential as ownership proof.
If verification fails, confirm that you copied the current token exactly. The HTTP file must be public, return valid JSON with a successful HTTP response, and stay on the connector's origin. DNS changes may need more time to propagate. A claim cannot transfer to a different origin or hostname: if the connector target changes, Glama starts the grace period and the new target must be claimed separately after the previous claim is released.
For a connector linked to the official MCP Registry, registry updates continue to replace its name, description, and URL by default. After claiming, open Manage connector and enable Use Glama listing details as the source of truth if edits made on Glama should be preserved. Categories and thumbnails are always managed on Glama; registry linkage and technical connection settings continue to sync.
Control your server's listing on Glama, including description and metadata
Access analytics and receive server usage reports
Get monitoring and health status updates for your server
Feature your server to boost visibility and reach more users
To improve your MCP server's ranking:
Claim ownership of the server listing
Complete the server profile with an accurate description and thumbnail
Provide a test profile so Glama can connect to and evaluate the server
Keep tool definitions clear and complete to earn a high Tool Definition Quality Score (TDQS)
Route real usage through the Glama Gateway; more recorded successful server uses also improve the ranking
For users:
Full audit trail – every tool call is logged with inputs and outputs for compliance and debugging
Granular tool control – enable or disable individual tools per connector to limit what your AI agents can do
Centralized credential management – store and rotate API keys and OAuth tokens in one place
Change alerts – get notified when a connector changes its schema, adds or removes tools, or updates tool definitions, so nothing breaks silently
For server owners:
Proven adoption – public usage metrics on your listing show real-world traction and build trust with prospective users
Tool-level analytics – see which tools are being used most, helping you prioritize development and documentation
Direct user feedback – users can report issues and suggest improvements through the listing, giving you a channel you would not have otherwise
The connector status is unhealthy when Glama is unable to successfully connect to the server. This can happen for several reasons:
The server is experiencing an outage
The URL of the server is wrong
Credentials required to access the server are missing or invalid
If you are the owner of this MCP connector and would like to make modifications to the listing, including providing test credentials for accessing the server, please contact support@glama.ai.
Discussions
No comments yet. Be the first to start the discussion!
Related MCP Connectors
Truck & trailer load planning from a plain-English shipment: what fits, how full, 3D load plan.
11Load planning for containers, trucks and pallets from a plain-English description; 3D plan link.
11Plain-English shipment input for freight & logistics: containers your cargo fits, 3D load plan.
Pack cargo into containers & onto pallets; a verifiable 3D loading plan. Free tier + REST API.
Related MCP Servers
- AlicenseAqualityCmaintenanceEnables 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.319MIT
- AlicenseAqualityBmaintenanceEnables optimal freight-mode selection (air, sea-LCL, full container) by solving 3D bin-packing, cost optimization, and delivery-risk analysis behind two tools: solve for deterministic planning and explain for hallucination-proof natural-language narration of results.2MIT
- AlicenseAqualityAmaintenanceAI agent access to 11 freight calculation and reference tools — LDM, CBM, chargeable weight, pallet fitting, ADR dangerous goods (2,939 entries), airline codes (6,352), HS codes (6,940), INCOTERMS, container specs, unit converter, and ADR 1.1.3.6 exemption calculator.252914MIT
Try in Browser
Glama MCP Gateway
Add one secure layer between your agents and this server.
TDQS
A4.7/5.0
Disambiguation5/5
With only one tool, there is no possibility of confusing or overlapping purposes. The single pack_shipment operation unambiguously owns the entire packing task.
Naming Consistency5/5
The sole tool name follows the clear verb_noun pattern: pack_shipment. There are no competing naming conventions or inconsistent verb styles to create confusion.
Tool Count4/5
A one-tool server is slightly on the thin side, but this tool is substantial and fully aligned with the server's stated purpose. It is not an extreme mismatch, though additional tools like listing container types or retrieving past load plans would make the surface feel more complete.
Completeness5/5
The tool covers the full packing workflow: it accepts cargo dimensions, weights, and constraints, selects containers, reports utilization, identifies non-fitting items, and provides a visual load plan. There are no obvious dead ends or missing operations within the server's stated domain.