About Hansatic
Hansatic is an MCP server for AI agents that submits cargo manifests to the Hansatic packing API and returns validated truck or container layouts. It accounts for physical loading constraints and provides freight metrics such as LDM, linear feet, and EUR pallet positions.
Key features
- 01Optimize cargo manifests for vehicles
- 02Respect rotation, stacking, fragility, weight, and axle constraints
- 03Return LDM, linear feet, and EUR pallet positions
- 04List built-in trucks, trailers, and containers
- 05Poll long-running packing jobs
Use cases
- 01Plan a truck load from a cargo manifest
- 02Check available vehicle dimensions and payloads
- 03Retrieve results for a large packing job
MCP support
The MCP server exposes `pack_cargo`, `list_vehicles`, and `check_job` tools to MCP clients. It connects to the Hansatic packing API using an API key.
Configuration
Local server config is the same for every client. Copy it into your client’s MCP config file.
Tools
3Saved tool list · updated 3w ago
| Parameter | Type | Required | Description |
|---|---|---|---|
| vehicle | string | Yes | Vehicle code, e.g. CURTAINSIDER_136 (13.6m curtainsider) or 40HC (40ft high-cube container). Get valid codes from list_vehicles. |
| items | array | Yes | The cargo manifest |
| objective | enum | No | Minimize loading meters (default) or billed pallet positionsldm | pallets |
| units | enum | No | How item numbers are read AND returned: mm/kg (default) or in/lbmetric | imperial |
| sandbox | boolean | No | Force sandbox mode (nothing persisted). Omit to let the account tier decide: enterprise accounts create a real plan with a shareable link, others sandbox automatically. |
| name | string | No | Plan name (production mode) |
| ldm_limit_m | number | No | Restrict the load to the first N loading meters (trucks) |
Details
From the project READMEHansatic MCP — load planning for AI agents
Give your AI agent the ability to pack trucks and shipping containers. This MCP server wraps the Hansatic packing API: submit a cargo manifest, get back a physically valid layout — placements with rotation, stacking, fragility, weight and axle constraints respected — plus the freight billing metrics that matter: LDM (loading meters), linear feet, and EUR pallet positions.
"Pack 14 machine crates and 8 EUR pallets into a 13.6m curtainsider" → a validated 3D load plan with 5.25 LDM, in one tool call.
Setup
-
Get an API key (free): hansatic.com → sign in → click your name → API → Generate key. Every account includes 25 free sandbox packs/month — no subscription needed to try it.
-
Add the server to your MCP client:
Claude Code
claude mcp add hansatic -e HANSATIC_API_KEY=hk_live_... -- npx -y hansatic-mcp
Claude Desktop (claude_desktop_config.json)
{
"mcpServers": {
"hansatic": {
"command": "npx",
"args": ["-y", "hansatic-mcp"],
"env": { "HANSATIC_API_KEY": "hk_live_..." }
}
}
}
Cursor (.cursor/mcp.json) — same shape as Claude Desktop.
Tools
| Tool | What it does |
|---|---|
pack_cargo | Optimize a cargo manifest into a vehicle. Async under the hood; returns the finished layout + metrics in one call (typically 5–30s). Production accounts get a plan_url — the layout opens in Hansatic's 3D editor. |
list_vehicles | All built-in trucks, trailers and containers with interior dimensions and payload — the valid vehicle codes. |
check_job | Poll a job by id (only for very large manifests that outlive the tool timeout). |
Notes
- Units: metric (mm/kg) by default; pass
units: "imperial"for in/lb — results come back in the same units. - Sandbox vs production: non-enterprise keys sandbox automatically (full
result, nothing persisted). Enterprise keys create a real plan in the
workspace, editable at the returned
plan_url. - The engine is validated continuously against real dispatcher layouts — see how the algorithm is measured.
- REST API, OpenAPI spec and full docs: hansatic.com/en/docs
MIT licensed. The optimization engine runs server-side at hansatic.com.
Comments
New to MCP?
From what the protocol is to installing your first server, these answers cover most first questions.
An open protocol that defines how AI applications connect to external tools and data in a uniform way. Clients talk to the model; MCP servers provide tools, resources and prompt templates. They exchange standard messages, so one server can be reused by many clients.