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Fusion360 MCP Server

Fusion360 MCP Server

by forgemorrow
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Local

Set Appearance

set_appearance
Idempotent

Assign a material appearance from the Fusion appearance library to a body, face, or component to define its visual finish in CAD designs.

Instructions

Assign a material appearance to a body, face, or component from the Fusion appearance library

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": {
      "target_name": "<target_name>",
      "appearance_name": "<appearance_name>"
    },
    "name": "set_appearance"
  }
}

Input Schema

NameRequiredDescriptionDefault
face_indexNoFace index (if target_type=face)
target_nameYesName of body or component
target_typeNobody
appearance_nameYesLibrary appearance name (e.g. 'Steel - Satin', 'Aluminum - Anodized Red')

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

B3.3/5.0
Behavior3/5

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

Annotations indicate this is a non-destructive, idempotent mutation tool. The description adds value by specifying the source ('Fusion appearance library') and providing examples of appearance names, which helps contextualize the operation. However, it doesn't disclose additional behavioral traits like permission requirements, error conditions, or side effects.

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?

The description is a single, well-structured sentence that efficiently conveys the tool's purpose without redundancy. It front-loads the key action and target, making it easy to parse.

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?

For a mutation tool with no output schema, the description is moderately complete. It covers the what and where but lacks details on outcomes, error handling, or integration with sibling tools. Given the annotations provide safety context, it's adequate but leaves gaps in operational guidance.

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?

With 75% schema description coverage, the baseline is 3. The description adds minimal semantics by mentioning 'library appearance name' and giving examples, but it doesn't explain parameter interactions (e.g., face_index only used with target_type=face) or provide context beyond what the schema already covers.

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?

The description clearly states the action ('Assign a material appearance') and the target ('to a body, face, or component'), with specific reference to the 'Fusion appearance library'. It distinguishes from siblings by focusing on appearance assignment rather than geometric operations or other functions, though it doesn't explicitly name alternatives.

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

Usage Guidelines2/5

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

The description provides no guidance on when to use this tool versus alternatives. It doesn't mention prerequisites, context (e.g., after creating geometry), or exclusions, leaving the agent to infer usage based on the tool name and parameters alone.

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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