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validate_voice_leading

Check multi-voice passages for parallel fifths, octaves, voice crossing, and range issues. Ensures proper voice leading in choral or harmonic writing.

Instructions

Validate voice leading in a multi-voice passage.

Checks for parallel fifths/octaves, voice crossing, and range issues.

Args: voices: Dictionary mapping voice names to pitch lists Example: {"soprano": ["C5", "D5"], "alto": ["E4", "F4"], "tenor": ["G3", "A3"], "bass": ["C3", "D3"]} key: Key for analysis (e.g., "C major", "A minor")

Returns: Validation results with errors and warnings

Examples: - Four-part: validate_voice_leading({ "soprano": ["C5", "D5", "E5"], "alto": ["E4", "F4", "G4"], "tenor": ["G3", "A3", "B3"], "bass": ["C3", "D3", "E3"] })

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
voicesYes
keyNoC major

Output 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 of disclosing behavioral traits. It describes what validations are performed but does not mention side effects, authentication needs, rate limits, or limitations. The behavioral disclosure is adequate but not comprehensive.

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-structured with a main sentence, a bulleted list of checks, an Args section, Returns, and an Example. It is moderately concise; the example is somewhat lengthy but informative. The structure helps quick understanding.

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?

The tool has moderate complexity with two parameters, nested objects, and an output schema. The description covers parameters and returns adequately. However, it fails to differentiate from the similar sibling 'check_voice_leading', leaving the agent without context on which to use.

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 has 0% description coverage, so the description fully compensates. It explains the 'voices' parameter as a dictionary mapping voice names to pitch lists with an example, and the 'key' parameter with a default value. This adds significant meaning beyond the raw schema.

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 clearly states the verb 'validate' and the resource 'voice leading', listing specific checks (parallel fifths/octaves, voice crossing, range issues). However, it does not differentiate from the sibling tool 'check_voice_leading', which likely performs a similar function.

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?

The description provides an example of usage but no explicit guidance on when to use this tool versus alternatives. It does not state when not to use it or mention any prerequisites or conditions.

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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