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mcp_opendaw_set_vocoder_band_count

Set the number of filter bands on a Vocoder effect in an audio chain. Provide the unit index, effect index, and band count (8-32) to control the vocoder's spectral resolution.

Instructions

Set the band count on a Vocoder effect (number of filter bands, typically 8-32).

unit_index: AU index. effect_index: Effect index in the audio effect chain (must be a Vocoder). band_count: Number of bands (8, 16, 24, 32 are common values).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
band_countYes
unit_indexYes
effect_indexYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
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 adds useful behavioral context (band count range and the vocoder requirement) but does not disclose side effects, error behavior, or what happens if the effect is not a Vocoder. This is minimal but not inadequate for a simple setter.

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 extremely concise: one sentence for purpose followed by three lines of parameter explanations. It is front-loaded with the main action and contains no filler, making every line earn 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?

For a simple setter tool with an output schema present, the description is quite complete: it covers purpose, parameter meanings, and the key constraint (must be a Vocoder). It does not detail error handling or return values, but the output schema likely covers that, so the description suffices for its complexity.

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?

The JSON schema has 0% description coverage, and the description manually explains all three parameters: unit_index ('AU index'), effect_index ('Effect index in the audio effect chain (must be a Vocoder)'), and band_count ('Number of bands (8, 16, 24, 32 are common values)'). This fully compensates for the schema's lack of descriptions.

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 the tool's action: 'Set the band count on a Vocoder effect' and adds context about typical values (8-32). This is a specific verb+resource combination that distinguishes it from other effect-specific setters in the sibling list.

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 provides the constraint that effect_index 'must be a Vocoder', which is useful context, but it does not explicitly mention when to use this tool over generic parameter setters like set_effect_parameter_int. There are no alternatives or when-not-to-use statements, so usage guidance is only implied.

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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