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mcp_opendaw_create_markov_melody

Generate melodies with stylistic memory using a Markov chain that models interval-to-interval tendencies, capturing natural genre or composer patterns.

Instructions

Create a melody using a Markov chain over scale-degree intervals.

First-order (or higher) Markov chain: the next interval depends on the current (or previous N) interval(s) via a transition probability matrix. This produces melodies with stylistic memory — the interval patterns characteristic of a genre or composer emerge naturally.

Unlike random_walk (zero-order: each step independent of history), Markov chains capture interval-to-interval tendencies:

  • A small ascending interval tends to be followed by another small one

  • A large leap tends to be followed by a step back (regression to mean)

  • Specific interval sequences define melodic "style"

The transition matrix can be:

  • Default: built-in weights favoring smooth motion (steps > skips > leaps)

  • Custom: user-provided interval weights as JSON

Args: root: Root note name (C, C#, D, ...). scale: Scale name (major, minor, dorian, phrygian, lydian, mixolydian, harmonic_minor, melodic_minor, pentatonic_major, pentatonic_minor, blues). bars: Number of bars (1-32). octave: Starting MIDI octave (1-6). order: Markov chain order (1 or 2). Order 1 = depends on current interval. Order 2 = depends on last 2 intervals. interval_weights: JSON string of custom transition weights. If empty, uses built-in weights. Format for order 1: {"-3": {"-3": 0.1, "-2": 0.2, "-1": 0.3, "0": 0.1, "1": 0.2, "2": 0.1}, "-2": {...}, ...} Keys are interval sizes (-7 to +7 scale steps). duration: Note duration in beats (0.0625-4.0). velocity: Base velocity 0-1. seed: PRNG seed for reproducibility. unit_index: AU index. track_index: Note track index. start_beat: Starting beat position.

Returns notes created, transition statistics, and seed.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
barsNo
rootNoC
seedNo
orderNo
scaleNominor
octaveNo
durationNo
velocityNo
start_beatNo
unit_indexNo
track_indexNo
interval_weightsNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior5/5

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

No annotations provided, so the description carries full burden. It thoroughly explains the Markov chain behavior, order options, transition probabilities, and mention of returns (notes, statistics, seed). No hidden behaviors.

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 with a summary, conceptual explanation, contrast, and parameter list. While long, each section adds value and no content is redundant. Could be slightly more concise but still efficient.

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?

Given 12 parameters and complexity, the description covers all behavioral aspects, parameter semantics, and expected outputs. The presence of an output schema further supports completeness.

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 description coverage is 0%, but the description provides detailed explanations for all 12 parameters in the Args section, including default values, formats (e.g., interval_weights JSON example), and allowed ranges.

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 the tool's purpose: 'Create a melody using a Markov chain over scale-degree intervals.' It explains the concept of Markov chains and contrasts with random_walk, making the unique value proposition evident.

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 advises when to use this tool (for stylistic memory) and explicitly contrasts with random_walk. It explains the transition matrix options (default vs custom) but does not list other alternatives or explicitly 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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