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Glama

Notes Transform

notes_transform

Edit note events in selected bars: transpose, quantize, set or scale velocity/duration, apply legato, shift timing, or limit to pitches for text-based music projects.

Instructions

Edit notes in bars [a, b]: transpose (semitones), velocity (set) / vel_scale, shift_beats, quantize (grid in beats; a note's @offset is kept, so a nudge survives quantizing), dur_scale / dur_set, legato (extend each note to the next onset), offset_ms (set each note's nudge in ms, negative = earlier; 0 removes it). pitches limits to those pitches.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
barsYes
trackYes
legatoNo
dur_setNo
pitchesNo
projectYes
quantizeNo
velocityNo
dur_scaleNo
offset_msNo
transposeNo
vel_scaleNo
shift_beatsNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.3.0

TDQS

B3.3/5.0
Behavior3/5

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

With no annotations, the description carries the full burden, and it does disclose useful per-operation behavior: quantize preserves a note's @offset so a nudge survives, legato extends each note to the next onset, and offset_ms with negative = earlier / 0 = removes. However, it says nothing about permissions, undo/reversibility, or whether the edit persists, which are significant gaps for a mutation tool with zero annotation coverage.

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?

Front-loaded with the scope ('bars [a, b]') followed by a dense enumeration of operations. It is a single long sentence that stays informative without padding, though the run-on structure slightly hurts scannability.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

An output schema exists, so return values need no explanation, and the operation semantics are well covered. But for a 13-parameter mutation tool with no annotations, the absence of any usage or safety context leaves the definition only adequate rather than complete.

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 description coverage is 0%, so the description must compensate, and it largely does: it explains transpose (semitones), velocity/vel_scale, shift_beats, quantize (grid in beats), dur_scale/dur_set, legato, offset_ms, and pitches semantics. Only project and track are left unexplained, which are self-evident strings, so the coverage is strong.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description opens with a specific verb and resource ('Edit notes in bars [a, b]') and enumerates the transformation operations, which distinguishes it from sibling mutation tools like notes_write (creating notes), notes_clear, and notes_copy. It is clear but never names or contrasts those siblings explicitly.

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

Usage Guidelines2/5

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

There is no guidance on when to choose this tool over notes_write, notes_copy, or pattern_write, nor any prerequisites or exclusions. The description lists available operations but leaves the selection decision entirely to inference.

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