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AudialAI

io.github.AudialAI/audial-mcp

Official
by AudialAI

Segment audio

segment

Detect song sections (intro, verse, chorus) and analyze components like vocals and drums within them.

Instructions

Detect song sections (intro, verse, chorus...) and analyze components within them.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
genreNoGenre hint that improves section detection.
featuresNoFeatures to compute per segment.
file_pathYesFull path to a local audio file (.wav, .mp3, .aif, .aiff, .flac, .m4a, .ogg, .aac). ~ is expanded.
componentsNoComponents to analyze, e.g. ['vocals', 'drums'].
analysis_typeNoAnalysis type accepted by Audial's segmentation.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
toolYes
filesYes
summaryYes
metadataYes
output_dirYes
execution_idYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.1

TDQS

B3.1/5.0
Behavior2/5

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

Annotations already declare readOnlyHint=false, openWorldHint=true, and idempotentHint=false. The description adds no behavioral context such as whether output files are created, whether repeated calls are safe, or what side effects to expect.

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?

A single front-loaded sentence with no redundant or filler text. Every word contributes to stating what the tool does.

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?

With an output schema present and full schema coverage, return values and parameters are handled elsewhere. However, for a tool with multiple sibling analysis/generation tools, the description omits when to use it versus alternatives and does not disclose enough behavioral context.

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 100%, so parameters like genre, features, components, and analysis_type are fully documented in the schema. The description mentions components generally but adds no syntax or format details beyond the 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?

States a specific verb+resource: detect song sections (intro, verse, chorus) and analyze components within them. The purpose is clear, but it does not explicitly distinguish itself from the sibling tool 'analyze' or 'stem_split'.

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?

No when-to-use guidance, no conditions for choosing this over siblings like analyze or stem_split, and no prerequisites beyond the schema. The description only implies usage from its function.

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