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Daz3D-MCP

Daz3D-MCP

by MictlanLabs
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Local

fit_check

Read-onlyIdempotent

Detect per-body-part clipping between a figure and furniture or other figures, reporting penetration depth and exit direction, or how far it floats above a surface.

Instructions

Checks a figure against furniture or other figures, per body part: how deep it goes into them and which way is out, or how far it floats above the surface under it.

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": {
      "figure": "<figure>"
    },
    "name": "fit_check"
  }
}

Input Schema

NameRequiredDescriptionDefault
wornNoAlso test clothing and hair.
figureYesFigure label.
againstNoProps or figures to test; default every visible prop touching the figure.
floor_yNoAlso report each foot's height above this floor height.
max_depthNoDeepest overlap that counts, cm.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.4.0

TDQS

A3.7/5.0
Behavior4/5

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

Annotations already establish read-only, idempotent, non-destructive, closed-world behavior, so the description does not need to restate safety. It instead adds genuinely new behavioral detail: results are reported per body part and distinguish penetration (depth plus out-direction) from floating above a surface. It does not disclose what happens when an overlap exceeds max_depth or the cost of testing against all touching props.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

A single front-loaded sentence that identifies the operation and then enumerates its outputs, with no filler. The nested colon-and-or construction is slightly dense but every clause carries information.

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

Completeness4/5

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

For a five-parameter tool with no output schema, the description usefully characterizes the return shape (depth, out-direction, float distance, per body part), which is the most important gap-filler. Remaining gaps—truncation at max_depth and the boundary with the clothing sibling—are minor but real.

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 coverage is 100%, so all five parameters including 'worn', 'against', and 'floor_y' are already documented in the schema. The description only echoes the 'against' and floor concepts at a high level and adds no syntax, units, or default behavior beyond what the schema states; the baseline of 3 applies.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

States a specific verb and resource ('Checks a figure against furniture or other figures') and adds per-body-part granularity plus the three measurements returned (penetration depth, exit direction, float height). What it lacks is differentiation from the closely named sibling wearable_fit, which is presumably the clothing/hair counterpart.

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

Usage Guidelines3/5

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

The metric it reports implies the use case (collision/placement validation), but there is no explicit when-to-use or when-not guidance and no alternative named. An agent must infer that the 'worn' parameter covers clothing/hair rather than spotting it in prose, and nothing routes it away from wearable_fit.

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

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