Skip to main content
Glama

mcp_opendaw_create_l_system_melody

Create deterministic, self-similar melodies by applying L-system production rules. Use presets or custom axioms for algorithmic fractal music.

Instructions

Create a melody using an L-system (Lindenmayer system) — a deterministic rewriting system.

L-systems generate self-similar, fractal patterns through recursive production rules. Each symbol in the expanded string maps to a scale step interval. The cumulative sum of intervals determines the melodic contour.

Unlike Markov chains (stochastic, memory-based) or random walk (zero-order), L-systems are fully deterministic — same axiom + rules + iterations always produce the same melody. This makes them ideal for:

  • Self-similar melodic structures (fractal music)

  • Deterministic generative composition

  • Algorithmic music based on mathematical systems

Presets: fibonacci — Fibonacci word (A->AB, B->A), golden ratio self-similarity cantor — Cantor set (A->ABA, B->BBB), gaps and self-similar structure dragon — Dragon curve (A->A+B, B->A-B), jagged contour koch — Koch snowflake (A->A+A-A-A+A), angular melody sierpinski — Sierpinski triangle (A->BA, B->BA), binary pattern

Custom: provide axiom, rules (JSON), and symbol_map (JSON) to define your own L-system.

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). preset: Preset name (fibonacci, cantor, dragon, koch, sierpinski). axiom: Custom axiom string (overrides preset). rules: Custom rules as JSON {"A": "AB", "B": "A"}. symbol_map: Custom symbol-to-interval map as JSON {"A": 1, "B": -1}. iterations: Number of rule applications (1-8). Higher = more complex. duration: Note duration in beats (0.0625-4.0). velocity: Base velocity 0-1. rest_symbol: Symbol that produces a rest (skip note, advance position). unit_index: AU index. track_index: Note track index. start_beat: Starting beat position.

Returns notes created, L-system string length, and fractal statistics.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
barsNo
rootNoC
axiomNo
rulesNo
scaleNominor
octaveNo
presetNofibonacci
durationNo
velocityNo
iterationsNo
start_beatNo
symbol_mapNo
unit_indexNo
rest_symbolNo
track_indexNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior3/5

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

No annotations are provided, so the description carries full burden. It explains that the tool is deterministic (same axiom+rules+iterations = same melody) and mentions the return value. However, it does not detail side effects (e.g., does it directly modify a track?), error conditions, or behavior for edge cases like invalid rules.

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: opening sentence, explanation of L-systems, comparison to other methods, presets list, custom options, then parameter list. It is slightly lengthy but front-loaded with essential information. Every section earns its place.

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 15 parameters and no output schema, the description covers the tool's purpose, how it works, and most parameter meanings. It mentions return value (notes, string length, statistics) but not exact structure. Overall, it is sufficiently complete for an agent to use the tool correctly.

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%, so description must compensate. It provides an 'Args' section explaining each parameter, including preset names, custom JSON formatting for rules and symbol_map, and typical defaults. Basic parameters like track_index are only glossed, but overall the description adds significant meaning beyond the naked 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 'Create a melody using an L-system' and explains the underlying concept. It distinguishes L-systems from Markov chains and random walk by highlighting determinism and self-similarity. Presets and custom options are detailed, making the purpose unambiguous.

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 contrasts L-systems with stochastic methods (Markov chains, random walk) and lists ideal use cases (self-similar, deterministic generative composition). It does not explicitly state when not to use it, but the differentiation from siblings is strong enough to guide selection.

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-team/opendaw-mcp'

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