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design_session

Turn plain-English physical object requests into printable 3D STL models, with quick clarifying questions and design checks.

Instructions

Make, design or create any physical object — a part, a bracket, a box, a holder, a gift — as a 3D model ready to print: Kiln's front door for making, where every make for a person starts. Say what they want in plain English (a specific request goes straight to generate; a vague one gets a quick question or two), then generate the STL from OpenSCAD you write (scad_code), with a report of which design checks it passes; every other make tool is a path this one routes to, and takes this goal's goal_id. Verbs: start, update, save, generate.

Input Schema

NameRequiredDescriptionDefault
ideaNoDefault: "".
verbYes
answersNo
goal_idNoDefault: "".
materialNoDefault: "".
scad_codeNoDefault: "".
spec_numbersNo
artifact_typeNoDefault: "design".
target_size_mmNo
decided_againstNo
override_promptNo
reference_imagesNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. Addedv1.5.0

TDQS

B3.2/5.0
Behavior3/5

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

With no annotations, the description carries the full burden. It does disclose meaningful behavior: plain-English intake, conditional question generation, STL produced from OpenSCAD (scad_code), and a report of which design checks pass. However, the four verb modes (start/update/save/generate) and their side effects, permissions, or reversibility are left unexplained.

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

Conciseness3/5

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

Front-loaded with the core purpose, which is good, but it is a dense run-on sentence with em-dashes and nested parentheticals that make it harder to parse than necessary. Each clause is somewhat useful, but the structure could be cleaner.

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

Completeness2/5

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

For a 12-parameter tool with no annotations, no output schema, low schema coverage, and nested objects, the description is under-specified. It outlines the workflow but omits verb semantics and the roles of most parameters, leaving gaps an agent would need to guess.

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 description coverage is only 42% across 12 parameters, so the description must compensate and does not. It mentions only scad_code and goal_id, leaving idea, answers, material, spec_numbers, artifact_type, target_size_mm, decided_against, override_prompt, and reference_images undocumented in both schema and text.

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 clear verb+resource ('make, design or create any physical object ... as a 3D model ready to print') and positions itself as the front door that other make tools route through. It differentiates from siblings by naming its routing role and the verbs (start, update, save, generate). Slightly sprawling, but the purpose is unmistakable.

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 concrete when-to-use context: 'every make for a person starts' here, and it routes to other make tools via goal_id. It further distinguishes a specific request (goes straight to generate) from a vague one (gets a quick question). No explicit when-not to use it, which keeps it below a 5.

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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