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fusion-mcp

timeline_feature_parameters

Retrieve a Fusion 360 feature's parameters and editable keys to inspect values and determine which arguments timeline edit tools accept.

Instructions

Model parameters created by a feature, and the keys its timeline_edit_* tool accepts.

Returns {"feature": ref, "parameters": [{"name", "expression", "value", "value_unit" ("mm", "deg", "" or "fusion_internal" for other units), "role", "comment"}], "editable_keys": [...]}.

Example: timeline_feature_parameters(feature=) -> [{"name": "d3", "expression": "20 mm", "value": 20.0, "value_unit": "mm", "role": "AlongDistance"}].

Input Schema

NameRequiredDescriptionDefault
featureYesFeature ref from timeline_list (or 'timeline:<name>')

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?

With no annotations, the description carries the full load, and it does the most important part: it spells out the return shape, including the value_unit enumeration ('mm', 'deg', '' or 'fusion_internal') which is non-obvious and would otherwise be a trap. It does not cover failure modes (invalid/ambiguous feature ref) or whether the call triggers a recompute.

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 return contract and a single concrete example — every block earns its place. The value_unit parenthetical is dense and the braces/parenthesis notation is slightly informal, but nothing is redundant.

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

Completeness5/5

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

There is no output schema, so the description must supply the return contract, and it does so completely: field list for each parameter object, the unit vocabulary, and the editable_keys array. Combined with the fully documented single input, an agent has everything needed to call and consume the result.

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% and the single parameter already documents its source ('Feature ref from timeline_list (or timeline:<name>)'). The description adds only an illustrative call with '<extrude ref>', which reinforces the ref format but adds no new constraint beyond 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?

The description states a specific resource — the model parameters created by a given feature — and adds the second purpose of exposing the keys that the timeline_edit_* siblings accept. That implicitly separates it from parameter_list (document-level parameters) and timeline_list (feature enumeration). It never uses an explicit verb like 'get'/'list', so the read-operation framing is inferred rather than stated.

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?

The mention of 'the keys its timeline_edit_* tool accepts' implies the intended workflow — call this before editing a feature to learn valid keys — but the description never says so directly, nor does it name when to prefer parameter_list or timeline_list instead. Usage is inferable from context rather than stated.

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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