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fusion-mcp

body_thicken

Converts selected faces, typically from a surface body, into a solid by applying a specified thickness and operation with unit-aware control.

Instructions

Thicken faces (usually of a surface body) into a solid.

Units: thickness in units (default mm) or a Fusion expression. Faces from inspect_query.

Errors: thickness 0, a non-face ref -> INVALID_PARAMS (nothing changed); a thicken Fusion cannot compute -> FUSION_API_ERROR, rolled back.

Returns {"feature", "health", "created_bodies", "modified_bodies", "removed_bodies"}.

Example: body_thicken(faces=[], thickness=2)

Input Schema

NameRequiredDescriptionDefault
chainNoAlso take tangent-connected faces
facesYesFace refs (inspect_query); re-query after later features
unitsNoUnit of plain-number lengthsmm
operationNonew_body (default), join, cut, intersect, new_componentnew_body
symmetricNoGrow to both sides
thicknessYesSigned thickness; negative = the other side

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A3.8/5.0
Behavior4/5

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

No annotations exist, so the description carries the full burden, and it does real work: it declares the failure modes and their atomicity ("INVALID_PARAMS (nothing changed)", "FUSION_API_ERROR, rolled back") and the exact return payload. It omits permission/selection preconditions, but the mutation semantics are unusually well disclosed.

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?

Purpose is front-loaded, followed by units, errors, return shape and an example, in a scannable labelled layout. Minor redundancy in restating the units default already present in the schema.

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?

With no output schema, the description correctly supplies the return payload, and it covers error behavior and the thickness/unit semantics for a 6-parameter mutation tool. It leaves the `operation` modes (join/cut/intersect) unaddressed in prose, which matters for predicting whether existing bodies are altered.

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

Parameters4/5

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 still adds meaning by clarifying that thickness may be a plain number in `units` or a Fusion expression, plus a concrete invocation example. `chain`, `symmetric`, `operation` and `faces` semantics are left entirely to the schema.

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 ("Thicken faces ... into a solid") with a scope qualifier ("usually of a surface body") that separates it from surface tools. It never names a sibling it supersedes or replaces, so the differentiation is implied 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 Guidelines3/5

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

"Faces from inspect_query" gives a workflow link and the input provenance, and the schema adds the re-query-after-later-features caveat. It does not say when to use this over feature_offset_faces, body_boundary_fill or a surface route, so usage is only implied.

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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