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edit_disjoin

Split audio at detected silences to create separate clips. Use this AudacityMCP tool to automatically separate audio segments for editing or organization.

Instructions

Split the selected audio at detected silences, creating separate clips.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Behavior3/5

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

With no annotations provided, the description carries the full burden. It discloses that the tool operates on selections and creates separate clips, implying a non-destructive split operation. However, it lacks details on what constitutes a 'silence' (threshold), error conditions (no selection, no silences detected), or whether the operation is reversible/undoable.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single, efficient sentence with zero wasted words. It front-loads the action ('Split'), specifies the target ('selected audio'), clarifies the method ('at detected silences'), and states the result ('creating separate clips'). Every clause 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 the tool has zero parameters and no output schema, the description adequately covers the core functionality for an automated editing operation. It explains what the tool does and its specific trigger mechanism (silence detection). It could be improved by noting that it uses automatic silence detection settings or mentioning selection requirements explicitly, but it is sufficient for invocation.

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?

The input schema contains zero parameters. According to calibration rules, zero-parameter tools receive a baseline score of 4. The description does not need to compensate for missing parameter documentation since there are no parameters to document.

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 provides a specific verb ('Split'), target resource ('selected audio'), mechanism ('at detected silences'), and outcome ('creating separate clips'). The phrase 'at detected silences' effectively distinguishes this tool from siblings like edit_split, edit_split_cut, and edit_join by specifying the automatic detection behavior.

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?

The description implies usage context (when you want to split audio based on silence detection) and implies prerequisites ('selected audio' suggests a selection must exist). However, it does not explicitly contrast with alternatives like edit_split or state when NOT to use this tool versus manual splitting methods.

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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