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Create probabilistic sequencer

create_prob_sequencer

Build a Markov-chain step sequencer that outputs state and trigger signals for evolving probabilistic rhythms and generative state machines.

Instructions

Build a Markov-chain step sequencer. On each beat boundary the COMP transitions from the current state to a next state sampled from the per-state weighted-transition table. Outputs two CHOP channels: 'state' (current state index) and 'trigger' (pulse on state change). Generative sibling of create_euclidean_sequencer and create_beat_grid_sequencer — great for evolving, probabilistic rhythms and generative state machines. NOTE: beat-callback timing requires a live TD session with time.play=1.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
bpmNoTempo written to Beat CHOP when no bpm_source is provided.
nameNoContainer COMP name.prob_seq
seedNoIf set, seeds Python random for reproducible runs.
statesYesMarkov states. Each state has a unique id, a weight (initial distribution), and a transitions map (keys = state ids, values ≥ 0).
divisionNoBeat subdivision (1/4→1, 1/8→2, 1/16→4 beats-per-measure).1/8
bpm_sourceNoPath to an existing Beat CHOP / tempo source. Omit to build a new one.
startStateNoInitial state id. If omitted, sampled from state weights.
parent_pathNoParent COMP path./project1
Behavior4/5

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

Annotations only indicate open-world and non-destructive behavior, so the description carries the burden of explaining runtime behavior. It discloses the two output channels ('state' and 'trigger'), the Markov-chain transition mechanism, and the critical requirement of a live TD session with time.play=1. This goes beyond annotations, though it could also mention that a new COMP is created in the network.

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 long and every sentence earns its place: the first states the core purpose, the second details the behavior and outputs, and the third provides sibling context, use-case guidance, and an important runtime note. It is front-loaded and free of filler.

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?

For an 8-parameter generative tool with no output schema, the description covers the core algorithm, created outputs, runtime prerequisite, and sibling relationships. It omits explicit mention of the created COMP name or parent path, but those are in the schema. The note about time.play is crucial for live-session usage, making the description reasonably complete.

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 the baseline is 3. The description adds context about how the 'states' parameter works ('per-state weighted-transition table') and mentions 'beat boundary' which relates to 'division' and 'bpm', but it does not provide additional syntax or per-parameter detail beyond what the schema already gives.

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 opens with 'Build a Markov-chain step sequencer,' which is a specific verb+resource statement. It further distinguishes itself from siblings by labeling itself a 'generative sibling' and explaining its probabilistic nature, making it clearly different from deterministic sequencer tools.

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 positions the tool for 'evolving, probabilistic rhythms and generative state machines,' and notes it is a 'generative sibling' of Euclidean and beat-grid sequencers, implying alternative deterministic use cases. However, it does not explicitly state when not to use it or name explicit alternative conditions beyond the sibling reference.

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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