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dsh18235538266-crypto

onshape-mcp-codex

create_shell

Hollow out a solid body into a thin-walled shell by removing selected faces. Preserves the outer bounding box with inward offset by default.

Instructions

Hollow out a solid body into a thin-walled shell. Pass the face IDs (from list_entities) you want REMOVED; the remaining faces become the shell wall. Inward by default — the outer bounding box is preserved and material is eaten inside. Returns the standard {ok, status, feature_id, changes?} contract.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
nameNoShell nameShell
faceIdsYesDeterministic IDs of faces to REMOVE. Everything else keeps the shell wall.
outwardNoIf true, offset the shell OUTSIDE the original surface (grows bbox). Default false (inward, preserves bbox) — the common enclosure case.
elementIdYesPart Studio element ID
thicknessYesWall thickness. Bare numbers are mm; use "1.5 mm" / "0.0625 in" for explicit units.
documentIdYesDocument ID
workspaceIdYesWorkspace ID
variableThicknessNoOptional variable name for thickness

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A4.2/5.0
Behavior4/5

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

With no annotations, the description carries the full behavioral burden. It discloses the inward default, that the outer bounding box is preserved, that material is removed inside, and the return contract. It does not cover failure modes or prerequisites like requiring a closed solid, so it is not perfect, but it is notably transparent.

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?

Three sentences with no wasted words. The purpose is front-loaded, the key input instruction follows, and the default behavior plus return contract close it out efficiently.

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?

Given 8 parameters, no annotations, and no output schema, the description covers the core semantics well: what the tool does, which faces to remove, default direction, and what is returned. Some edge-case behavior is left unspecified, but the essential agent guidance is present.

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 the baseline is 3. The description mostly restates faceIds semantics already in the schema, adding only the list_entities source and the return contract, which is not parameter-level detail. It adds marginal value but does not substantially extend the schema.

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: "Hollow out a solid body into a thin-walled shell." It clearly differentiates this from siblings like create_thicken and create_extrude by explaining the face-removal mechanism and the resulting wall behavior.

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?

It provides clear operational guidance: pass face IDs from list_entities, remove those faces, keep the rest as the wall, and inward is the default. It lacks explicit exclusions or named alternatives, but the context makes the primary use case unambiguous.

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