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ai_dj_set

Generate a complete DJ set from a theme: create tracks, analyze BPM and key, order for harmonic mixing, load to VirtualDJ decks, and configure automix.

Instructions

Generate a complete AI DJ set from a theme.

This workflow:

  1. Generates multiple tracks based on theme variations

  2. Analyzes BPM and key of each

  3. Orders tracks for harmonic mixing

  4. Loads to VirtualDJ decks

  5. Configures automix

  6. Optionally starts recording

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
themeYesOverall theme for the set Example: "progressive house summer festival"
bpm_rangeNoBPM range for generated tracks
num_tracksNoNumber of tracks to generate (2-8)
duration_minutesNoTarget set duration

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

B3.2/5.0
Behavior3/5

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

With no annotations, the description carries the full behavioral burden, and it does disclose a lot: this is a multi-step pipeline that generates audio, analyzes BPM/key, loads tracks to VirtualDJ decks, and reconfigures automix, with optional recording. However, it omits preconditions (VirtualDJ must be running), whether it overwrites existing deck/automix state, how long it runs, and whether progress is streamed or the call blocks.

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 one-line purpose is front-loaded and the numbered list earns its place by sequencing a genuinely multi-stage operation. It is slightly longer than strictly necessary, but no sentence is filler and the ordering is informative.

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 values need not be restated, and the workflow covers the main pipeline. What is missing for a tool of this complexity with real side effects is the operating envelope: prerequisites, failure behavior when VirtualDJ or generation services are unavailable, and interaction with pre-existing deck state.

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 theme, bpm_range, num_tracks, and duration_minutes are already documented with defaults and constraints in the schema. The description adds no parameter-level meaning beyond that, which matches the baseline 3 for a fully-described 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 (Generate) and a specific artifact (a complete AI DJ set) sourced from a theme, and the six-step workflow makes the scope concrete (track generation, harmonic ordering, deck loading, automix) in a way that separates it from single-track siblings like ai_produce_track or songgen_to_deck. It stops short of naming an alternative explicitly, so it is clear but not fully differentiated.

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?

There is no when-to-use guidance, no contrast with alternatives such as album_factory, bpm_bridge_generator, or ai_produce_track, and no exclusions. The workflow list implies the tool is an end-to-end pipeline, but the agent must infer that routing decision on its own.

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