formlabs-local-mcp

pack_and_cage

Pack 3D models and generate a printable cage around them to keep parts together after SLS printing. Operates on the active scene, with packing strategy options.

Instructions

Pack models and build a printed cage around them so they stay together after SLS printing. SLS only; acts on the most recently created scene. packing_type is PACK_VOLUME (default), PACK_HEIGHT, PACK_NORMAL or PACK_NONE. Returns the scene.

Tool call template

This tool declares no required parameters. Add optional parameters from the input schema as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {},
    "name": "pack_and_cage"
  }
}

Input Schema

Name	Required	Description	Default
`models`	No	"ALL" or a list of model ids	ALL
`cage_label`	No
`packing_type`	No
`model_spacing_mm`	No

Schema Changelog

Changes observed during successful MCP inspections.

First observedv1.0.4Sep 16, 2026

TDQS

A4/5.0

Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

The annotations already indicate a non-read-only operation, and the description adds useful behavior: it builds a cage, packs models, acts on the most recent scene, and returns that scene. It does not contradict the annotations, though it leaves open whether prior cages are replaced.

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 compact and front-loaded with the action, then adds constraints, parameter detail, and return value in a few tight clauses. Every sentence earns its place with no filler.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

It states the return value and key constraints, which is good with no output schema. However, it omits prerequisites such as requiring an existing most-recent scene and loaded models, and it leaves cage_label and model_spacing_mm semantics under-defined.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters2/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Only one of four parameters has a schema description, and the description only adds meaning for packing_type by listing its enum values and default. cage_label and model_spacing_mm remain unexplained, including missing units for the spacing value. Low schema coverage means the description needed to compensate more than it does.

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 names a specific verb and resource ('Pack models and build a printed cage') and scopes the operation to SLS printing and the most recently created scene. This clearly differentiates it from generic packing/layout siblings like auto_pack.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description gives explicit context for when the tool applies: SLS only, and it acts on the most recently created scene. It does not name alternatives for other cases, but the constraints are clear enough for an agent to select it appropriately.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.