formlabs-local-mcp

label_model

Add text labels to 3D models by embossing or engraving. Specify position, orientation, font size, and depth to place text on a model's surface.

Instructions

Emboss or engrave text onto a model's surface. position is the label centre {x,y,z} in scene mm; orientation (Euler degrees) sets the text direction with +x along the text and +z as the surface normal.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "model_id": "<model_id>",
      "label": "<label>",
      "position": "<position>",
      "font_size_mm": "<font_size_mm>",
      "depth_mm": "<depth_mm>"
    },
    "name": "label_model"
  }
}

Input Schema

Name	Required	Description	Default
`label`	Yes
`depth_mm`	Yes
`model_id`	Yes
`position`	Yes
`scene_id`	No	Scene id; omit for the default scene	default
`orientation`	No	Euler degrees {x,y,z}, or {z_direction:[..], x_direction:[..]} unit vectors
`font_size_mm`	Yes
`application_mode`	No		EMBOSS

Schema Changelog

Changes observed during successful MCP inspections.

First observedv1.0.4Sep 16, 2026

TDQS

A4.1/5.0

Behavior4/5

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

Beyond the readOnlyHint=false and destructiveHint=false annotations, the description adds meaningful behavioral detail: position is a label centre in scene mm, and orientation uses Euler degrees with explicit text direction and surface-normal conventions. It does not mention permanence or failure conditions, but the coordinate and orientation semantics are valuable.

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 two sentences with no filler. The core action is front-loaded, and the coordinate/orientation details are packed into a single follow-up sentence that directly supports correct invocation.

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?

For a tool with 8 parameters, nested objects, no output schema, and mutation annotations, the description gives a solid start but leaves gaps: it does not state the return value, the effect of depth_mm sign, or whether the operation modifies the model in place. It is sufficient for a first attempt but not fully complete.

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

Parameters3/5

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

Schema description coverage is only 25%, so the description needs to compensate. It does clarify position and orientation semantics, which are the two most ambiguous parameters, but it does not explain depth_mm, font_size_mm, label, model_id, or application_mode beyond their names/enum. This is partial compensation, not full.

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: 'Emboss or engrave text onto a model's surface.' This clearly distinguishes label_model from model-level operations like update_model, hollow_model, or auto_orient, and the two operation modes (EMBOSS/ENGRAVE) map directly to the application_mode enum.

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 makes the intended use obvious: adding text labels to a model. It does not explicitly name alternatives or exclusions, but no sibling tool appears to provide labeling behavior, so the context is clear enough for an agent to select this tool when text needs to be applied.

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