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mcp_opendaw_apply_swing

Apply swing to existing notes by shifting every other grid position later, creating a triplet or shuffle feel without altering velocity or duration. Adjust swing depth and grid resolution.

Instructions

Apply swing feel to existing notes without changing velocity or duration.

Swing shifts every other grid position later, creating a triplet/shuffle feel. Unlike humanize_notes (which couples swing with random velocity/timing changes), this tool applies pure swing — deterministic, no randomness, reversible.

unit_index: AU index (-1 = all AUs). track_index: Note track index (-1 = all note tracks on the AU). swing_amount: Swing depth 0-1 (0 = straight, 0.5 = light swing, 1.0 = full triplet). 0.55-0.66 = classic hip-hop/lofi swing. grid: Grid to swing against — "16th" (default, shifts odd 16ths) or "8th" (shifts odd 8ths).

Returns per-track note counts shifted.

Example: apply_swing(unit_index=0, track_index=0, swing_amount=0.58, grid="16th")

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
gridNo16th
unit_indexNo
track_indexNo
swing_amountNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior4/5

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

With no annotations provided, the description clearly states the tool is deterministic, reversible, and does not change velocity or duration. It does not mention potential side effects or limitations, but the stated properties give good behavioral context for an AI agent.

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?

The description is well-structured: a brief summary sentence, then an explanation of the effect, sibling differentiation, parameter details, and an example. It is slightly verbose in the parameter section but front-loaded with the main purpose. Every sentence adds value.

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?

All four parameters are explained, the behavior is described, and a usage example is given. The return value is briefly mentioned ('Returns per-track note counts shifted'), which is sufficient given the presence of an output schema. The tool is non-destructive and reversible, so no additional safety context is needed.

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

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 0%, so the description fully compensates by explaining each parameter in detail: unit_index, track_index, swing_amount (with specific ranges), and grid (valid values). It also provides an example usage. This adds significant meaning beyond the input 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 clearly states it applies swing feel to existing notes without changing velocity or duration, and distinguishes itself from the sibling tool 'humanize_notes' which couples swing with random changes. The verb 'apply' and resource 'swing feel to notes' are specific.

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?

The description explicitly compares to 'humanize_notes' and states this tool applies pure, deterministic, reversible swing. It provides guidance on swing_amount values (e.g., 0.55-0.66 for hip-hop/lofi). However, it does not explicitly state when not to use this tool or when to prefer alternatives beyond that single comparison.

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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