Skip to main content
Glama
AudialAI

io.github.AudialAI/audial-mcp

Official
by AudialAI

Generate a sample pack

generate_samples

Create a sample pack of one-shots and loops from any audio track, optionally specifying genre, components, or job type.

Instructions

Extract a sample pack (one-shots and loops) from a track.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
genreNoGenre hint.
job_typeNoSample pack job type accepted by Audial (default engine choice when omitted).
file_pathYesFull path to a local audio file (.wav, .mp3, .aif, .aiff, .flac, .m4a, .ogg, .aac). ~ is expanded.
componentsNoWhich components to sample, e.g. ['drums', 'bass'].

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 declare a non-read-only, non-idempotent, open-world operation, but the description adds no behavioral context: it does not say that output files are produced, whether the call runs as an async job (implied by job_type and the list_results sibling), how to retrieve results, or how long it takes. For a mutating tool with annotations only sketching the safety profile, this leaves a real gap.

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 filler; the deliverable is named immediately. Nothing in it is redundant.

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?

An output schema exists, so return-value explanation is not required, and all four parameters are documented in the schema. However, for a non-idempotent job-style tool that feeds a list_results workflow, the description omits async/result-retrieval context, leaving the agent to discover it elsewhere.

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 genre, job_type, file_path, and components are already documented in the schema. The description's mention of 'one-shots and loops' loosely maps to components but adds no format, required/optional, or default information beyond what the schema states. Baseline 3 applies.

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 (Extract), resource (sample pack), and expands the resource's contents (one-shots and loops) with a scope (from a track). An agent knows what comes out, but the description never distinguishes this from the closest sibling, stem_split, which also decomposes a track.

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 statement of when to use this over stem_split, analyze, or segment, and no prerequisites or exclusions. The only routing signal is the tool name itself, so the agent must infer intent from the sibling list.

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