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mcp_opendaw_create_motif_variations

Extract a motif from existing notes and generate classical composition variations like sequence, inversion, retrograde, augmentation, diminution, or fragmentation in a new region.

Instructions

Extract a motif from existing notes and create a variation in a new region.

Closes the analysis→creation loop: extract_motifs finds repeating patterns, this tool takes a specific motif and transforms it using classical composition techniques. The motif is identified by start_note index and note_count within the source region.

Variation types:

  • sequence: repeat the motif shifted up/down by N scale steps or semitones. Creates melodic sequences — the backbone of classical and jazz improvisation.

  • inversion: flip the contour upside down. C→E→G (+4, +3) becomes C→A→F (-3, -2). The intervals are mirrored around the first note.

  • retrograde: play the motif backwards. Last note first, first note last. The rhythm and pitches are reversed in time.

  • augmentation: stretch all durations by a factor (2.0 = twice as slow). Creates grand, expansive statements from quick motifs.

  • diminution: compress all durations by a factor (2.0 = twice as fast). Creates urgency and energy from slow motifs.

  • fragmentation: take the first N notes of the motif and repeat them. Creates rhythmic ostinatos from melodic material.

Essential for: developing melodic material, building variations, creating thematic development, and extending motifs into new sections.

source_unit/track/region: Location of the source motif. start_note: Index of the first note of the motif within the source region (0-based, sorted by position). note_count: Number of notes in the motif (3-16). target_unit/track/region: Where to write the variation. -1 = create new track/region automatically. variation_type: sequence, inversion, retrograde, augmentation, diminution, fragmentation. sequence_shift: For sequence type — semitones to shift each repetition. augmentation_factor: For augmentation/diminution — duration multiplier. fragment_count: For fragmentation — how many notes to keep from the start.

Returns the created variation with note details.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
note_countNo
start_noteNo
source_unitNo
target_unitNo
source_trackNo
target_trackNo
source_regionNo
target_regionNo
fragment_countNo
sequence_shiftNo
variation_typeNosequence
augmentation_factorNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior4/5

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

With no annotations, the description details each variation type, non-destructive creation in a new region, and return value. It omits error handling but is fairly transparent.

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-organized with sections for purpose, variation types, and parameters. It is slightly verbose but each part 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?

Given the tool's complexity (12 parameters) and existence of output schema, the description covers core functionality, use cases, and parameter details adequately.

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?

Since schema coverage is 0%, the description fully explains all 12 parameters with clear meanings, including variation-specific ones like sequence_shift, augmentation_factor, and fragment_count.

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 extracts a motif and creates a variation in a new region. It uses specific verbs and distinguishes from sibling tools like extract_motifs.

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 explains it closes the analysis→creation loop and positions itself after extract_motifs. It also lists use cases but lacks explicit when-not-to-use guidance.

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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