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Fannon

u-he-preset-randomizer-mcp

generate_random_presets

Generate random presets based on parameter statistics from your preset library, optionally filtered by author, category, pattern, or favorites for focused sound generation.

Instructions

Generate fully random presets based on parameter statistics from the loaded preset library. Optionally filter the statistical basis by author, category, pattern, or favorites to generate more focused/coherent sounds (e.g., random bass presets). Generated presets are automatically added to the library for immediate use with search_presets and explain_preset. Defaults to 16 presets if amount is omitted.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
amountNoNumber of random presets to generate. Defaults to 16.
authorNoFilter statistical basis by author name (exact match). Use presets from specific author as inspiration.
stableNoUse stable randomization (randomizes per-section to maintain coherence). Defaults to true.
patternNoOptional glob pattern to filter which presets to use as statistical basis (e.g., "Bass/**/*", "**/*Pad*").
categoryNoFilter statistical basis by category prefix (e.g., "Bass", "Bass:Sub"). Generate random sounds in specific category style.
favoritesNoFilter statistical basis by favorites file name (e.g., "MyFavorites.uhe-fav"). Use only favorited presets as inspiration.
dictionaryNoUse dictionary of meaningful names from preset library. Defaults to true.
Behavior4/5

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

The description discloses key behaviors: presets are automatically added to the library, default quantity, and stable randomization. Without annotations, it covers main traits but lacks info on error conditions or empty library handling.

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 three sentences, front-loaded with core purpose, then filtering options, then behavioral detail. No redundant information.

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?

The description covers generation, filtering, defaults, and integration with other tools. However, it omits prerequisites (e.g., loaded library) and error handling, which is minor given the clear output behavior.

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 coverage is 100%, so baseline is 3. The description adds context with filtering examples and default behavior but does not significantly supplement the schema's parameter 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 it generates random presets based on library statistics, with optional filtering for focused sounds. The verb 'generate' and resource 'random presets' are specific, and it distinguishes from siblings like 'randomize_presets' by emphasizing statistical basis from the library.

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 provides clear usage context, including optional filters and defaults, but does not explicitly compare to sibling tools or state when not to use it. The example 'random bass presets' is helpful.

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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