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mcp_opendaw_randomize_note_chance

Randomize note chance values (0-100%) to create generative MIDI patterns where notes probabilistically play or mute, with modes like uniform, decreasing, increasing, sparse, or binary.

Instructions

Randomize note playback probability (chance) — generative variation.

Sets a random chance value (0-100%) for each note, controlling whether it plays on each run. This is the core of generative MIDI — patterns that are different every time while maintaining structure. Notes with chance=100 always play, chance=50 play half the time, chance=0 never play (silent ghost).

Perfect for:

  • Ghost notes that appear/disappear (drum variation)

  • Generative melodies where notes drop in/out

  • Call-and-response patterns with probabilistic responses

  • Evolving textures that change per iteration

mode: Distribution of chance values:

  • "uniform" — random between min_chance and max_chance, evenly distributed. Each note gets an independent random chance. Default mode.

  • "decreasing" — chance decreases linearly from max to min across the region. First notes are most likely, last notes least. Creates fade-out of probability — pattern dissolves.

  • "increasing" — chance increases from min to max. Pattern emerges from silence. Builds anticipation.

  • "sparse" — most notes get min_chance, but some get max_chance. Creates sparse texture with occasional hits. Good for ghost notes.

  • "binary" — each note gets either min_chance or max_chance (coin flip). Creates stark on/off patterns.

min_chance: Minimum chance value (0-100, default 50). max_chance: Maximum chance value (0-100, default 100). seed: Random seed for reproducibility.

Returns per-track note counts, chance range applied.

Example:

Ghost note variation — 30-80% chance

randomize_note_chance(unit_index=0, track_index=0, min_chance=30, max_chance=80)

Dissolving pattern — high to low

randomize_note_chance(unit_index=0, track_index=2, mode="decreasing", min_chance=0, max_chance=100)

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
modeNouniform
seedNo
max_chanceNo
min_chanceNo
unit_indexNo
track_indexNo
region_indexNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior3/5

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

No annotations provided, so description carries full burden. It explains chance semantics (0-100%), mode behaviors, and return value (per-track note counts). However, it does not state whether the operation is destructive or reversible, nor if it overwrites or adds to existing chance values.

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 structured with a summary, mode details, parameter list, and examples. Every section adds value without unnecessary repetition. Front-loads the key concept and is easy to scan.

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?

Given 7 parameters, no annotations, and an output schema, the description covers the core functionality well. It explains chance, modes, parameters, return value, and provides examples. Missing explanation for index parameters is a minor gap.

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 0% (description does not replicate schema), but the description adds detailed meaning for 4 of 7 parameters (mode, min_chance, max_chance, seed) through explanations and examples. It does not explain unit_index, track_index, or region_index, which are common across tools.

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 'Randomize note playback probability (chance) — generative variation' and elaborates with specific use cases (ghost notes, generative melodies, etc.). It distinguishes this tool from siblings like randomize_note_durations by focusing on chance/probability.

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 lists 'Perfect for:' scenarios (ghost notes, generative melodies, etc.) and details each mode with its intended effect (e.g., 'sparse' for ghost notes). While it lacks an explicit 'when not to use', the guidance is strong and contextual.

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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