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Glama

Sound Extract

sound_extract

Isolates a repeated sound by averaging its occurrences at a specific grid step across bars, aligning by cross-correlation and reporting consistency.

Instructions

Build a clean one-shot of a repeated event by averaging every occurrence at grid position step (0-based within the bar, 16ths by default) over bars [a, b] (which='all'|'odd'|'even'; every=8 takes bars a, a+8, a+16... i.e. the same position of an 8-bar loop), aligned by cross-correlation. Sounds that repeat identically (drum samples, stabs) add up; everything else averages down. Reports how consistent the occurrences were.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
barsYes
nameYes
stepYes
everyNo
whichNoall
pre_msNo
sourceYes
projectYes
normalizeNo
length_secNo
steps_per_barNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.3.0

TDQS

A3.5/5.0
Behavior3/5

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

With no annotations, the description carries the full burden. It helpfully discloses the internal behavior (cross-correlation alignment, identical sounds summing while noise averages down) and that it 'reports how consistent the occurrences were,' but it omits whether a sound file is written, where, whether project state is mutated, and any permission or overwrite implications.

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?

It is a single dense paragraph that front-loads the purpose before the parameter details. Every sentence carries information, though the parenthetical parameter asides make it somewhat run-on and harder to scan.

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?

An output schema exists, so return values need not be described, and the description is appropriately silent there while adding the consistency-report note. Against a complex 11-parameter tool with 0% schema coverage, however, several parameters remain unexplained, leaving a moderate gap.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0% across 11 parameters. The description meaningfully explains `step`, `bars`, `which`, and `every` (including the 8-bar loop convention), but leaves source, project, name, pre_ms, normalize, length_sec, and steps_per_bar completely undocumented, so it only partially compensates for the coverage gap.

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 gives a specific verb and result ('Build a clean one-shot of a repeated event') plus the exact mechanism (averaging occurrences at grid position `step`, aligned by cross-correlation). It clearly conveys the operation's intent, though it never names or contrasts with sibling audio tools like sound_make or sound_import.

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

Usage Guidelines3/5

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

Usage is implied through the mechanism: it explains `which`, `every`, and the bars range, so an agent can infer this is for taming repetition in looping material. However, there is no explicit when-to-use versus alternatives (e.g., sound_make or sound_import) and no stated prerequisites.

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