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compose_and_reload

Generate a music composition using jazz, synthwave, or lofi styles and automatically reload it in Waveform, eliminating manual compose-revert steps.

Instructions

Dispatch one of jazz/synthwave/lofi generative composers AND revert in Waveform with one call. Avoids the compose-then-manually-revert dance during iteration.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
seedNo
tempoNo
composerYes
key_modeNo
key_rootNo
out_pathYes
target_secondsNo

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. First observedv0.1.0

TDQS

B3.2/5.0
Behavior2/5

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

With no annotations, the description must fully disclose behavioral traits. It mentions 'revert in Waveform' but does not explain what exactly is reverted, the side effects on the project or files, whether it is destructive, any prerequisites (like Waveform being open), or the return value. 'Dispatch' is vague about the underlying action. This is insufficient for a tool with no annotation support.

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 concise at two sentences, with no wasted words. It front-loads the core action and then provides a practical benefit. It could be slightly more structured (e.g., listing parameters) but it is appropriately sized for its purpose.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness2/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool has 7 parameters, no annotations, and no output schema, the description is far from complete. It omits parameter semantics, behavioral details, return values, and error conditions. An agent would not know how to properly invoke this tool beyond the basic intent. The description only covers the 'why' but not the 'how'.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters1/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The schema has 0% description coverage, so the description must compensate for all seven parameters. It does not mention any parameter except implicitly the composer (via the list), but provides no meaning for seed, tempo, key_mode, key_root, out_path, or target_seconds. The description adds no value to parameter understanding beyond the bare schema fields, which themselves have no 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 action: dispatch one of three named generative composers and revert in Waveform in a single call. It explicitly names the composer options (jazz/synthwave/lofi), which matches the schema enum, and contrasts with the separate compose tools among siblings by combining compose with revert. This distinguishes it from siblings like compose_lofi_generative or compose_jazz_generative.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description gives a clear context: it is for iteration, specifically to avoid the 'compose-then-manually-revert dance.' This implies when to use it (during iterative workflows where a revert is needed) but does not explicitly state when not to use it or name the alternative tools. It provides a strong reason for selection but lacks explicit exclusions.

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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