Skip to main content
Glama

mcp_opendaw_create_markov_melody

Generate a melody using a Markov chain over scale-degree intervals, capturing interval-to-interval stylistic tendencies. Supports custom transition weights for genre-specific melodic 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
Behavior4/5

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

No annotations are provided, so the description carries the burden. It explains the algorithmic behavior (Markov chain with transition matrix), the effect (melodies with stylistic memory), and the output ('Returns notes created, transition statistics, and seed'). It does not explicitly state non-destructiveness, but the 'create' verb and output description imply it. This is substantial but could be more explicit about side effects.

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 structured with a clear opening, an explanatory paragraph on Markov chains, and a parameter list. It is lengthy but every section earns its place given the tool's complexity and the need to compensate for sparse schema descriptions. It is not redundant; the example for interval_weights is particularly valuable.

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?

For a 12-parameter generative tool with no annotations and an existing output schema, the description is comprehensive. It covers the algorithm, parameter semantics, output, and a comparison to a sibling tool. There are few gaps; it even explains the significance of Markov order and the default weight behavior.

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 fully compensates with an 'Args' section explaining all 12 parameters. It provides details like the interval_weights JSON format with an example, root note naming, scale options, and order semantics. This goes far beyond the schema's titles and defaults.

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 opens with a specific verb+resource+method: 'Create a melody using a Markov chain over scale-degree intervals.' It clearly distinguishes from siblings like random_walk by contrasting zero-order vs higher-order behavior, making the tool's function unique.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description explicitly names an alternative: 'Unlike random_walk (zero-order: each step independent of history), Markov chains capture interval-to-interval tendencies.' It also explains when Markov chains are appropriate (stylistic memory) and covers default vs custom weight usage, giving clear when-to-use guidance.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Install Server

Other Tools

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/ameobius-ai/opendaw-mcp'

If you have feedback or need assistance with the MCP directory API, please join our Discord server