forge3d-mcp

Plan Model

plan_model

Turns vague 3D model requests into checkable specs before generation, defining purpose, units, dimensions, and missing details to request from the user.

Instructions

Turn a vague request into a checkable model spec, before generating anything.

purpose is print | web | game | cad | any and it changes what "correct" means. dimensions are real-world sizes in units, e.g. {"x": 180, "y": 90, "z": 75}. The response lists what is still missing — ask the user for those instead of inventing them.

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": "plan_model"
  }
}

Input Schema

Name	Required	Default
`mode`	No	auto
`name`	No	model
`style`	No
`units`	No	mm
`prompt`	No
`purpose`	No	any
`dimensions`	No
`references`	No
`poly_budget`	No

Output Schema

Name	Required	Description	Default
No arguments

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.6.7Nov 4, 2026

TDQS

B3.4/5.0

Behavior3/5

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

No annotations are supplied, so the description carries the full burden. 'Before generating anything' implies this call produces no artifact, but there is no explicit statement of side effects, idempotency, cost, or whether files/jobs are created; the only behavioral disclosure is that the response enumerates what is missing.

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?

Three short sentences, front-loaded with the purpose and ending with an actionable instruction; nothing is redundant. The line-wrapped raw formatting is slightly awkward but doesn't cost comprehension.

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?

An output schema exists, so the return envelope needn't be described, and the description correctly notes the response lists gaps. But for a 9-parameter planning tool that gates all generation, undefined parameters like mode, poly_budget, and references are a real completeness gap.

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?

Schema coverage is 0% across 9 parameters, and the description only explains purpose (with its allowed values), dimensions (with a literal example and unit binding), and units. Six parameters — mode, name, style, prompt, references, poly_budget — get no meaning at all, leaving the agent to guess at values like mode='auto' or poly_budget.

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: turn a request into a checkable model spec. The phrase 'before generating anything' implicitly separates it from the sibling create_model, but it never names the sibling, so the differentiation is inferred rather than explicit.

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?

Gives a clear prerequisite-style cue ('before generating anything') and an explicit operating instruction: request missing items from the user rather than inventing them. It stops short of naming the downstream tools (create_model, validate_model) it feeds into or stating when planning can be skipped.

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