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Fusion Native MCP

Fusion Native MCP

by Dawn-Of-Justice
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Local

fusion_create_drawing

Generate an automatic cloud drawing from a saved design. Configure standard, sheet size, and dimensions to produce the drawing.

Instructions

Experimental, not yet live-validated: create an automatic cloud drawing from an already saved design, with configurable standard, sheet and auto-dimensions. Requires the installed Drawing API; may start a cloud job.

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": {
      "document_id": "<document_id>",
      "operation_id": "<operation_id>",
      "spec": "<spec>"
    },
    "name": "fusion_create_drawing"
  }
}

Input Schema

NameRequiredDescriptionDefault
specYes
document_idYes
operation_idYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.2.0

TDQS

A3.9/5.0
Behavior3/5

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

No annotations are provided, so the description carries the transparency burden. It discloses that the tool is experimental, not yet live-validated, requires a specific API, and may start a cloud job. It does not explain what the agent should expect afterward, such as return values or how to track the started job.

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 compact and front-loaded with the important experimental warning and main action. Each sentence adds necessary information without filler or repetition.

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 tool with no output schema and no annotations, the description provides a reasonable but incomplete picture: it has a prerequisite, a caveat, and a side effect. It does not clarify how output/status is returned, how operation_id relates to the cloud job, or whether the call is asynchronous.

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 0% schema description coverage, the description must compensate for parameter understanding. It adds meaning by mapping the specification to standard, sheet, and auto-dimensions, which correspond to fields in the DrawingSpec object. However, document_id and operation_id remain unexplained, and the units field is omitted.

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 names a specific verb and resource: create an automatic cloud drawing from an already saved design. It also adds meaningful scope—configurable standard, sheet, and auto-dimensions—which clearly differentiates it from sibling tools like fusion_create_sketch or fusion_screenshot.

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 gives clear preconditions: the design must already be saved and the Drawing API must be installed. It also warns that the tool may start a cloud job, but it does not explicitly name alternatives or state when-not-to-use it.

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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