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Create visual system

create_visual_system

Transforms a natural-language description of a visual into a complete, verified node network with preview. Classifies intent and delegates to the appropriate builder.

Instructions

Create a complete visual system from a natural-language description. Classifies intent (audio-reactive, particle, feedback, reaction-diffusion, landscape, generative) and delegates to the matching Layer-1 builder (or a tag-matched recipe), creating a self-contained COMP under parent_path, then verifies and previews it. Use plan_visual instead for a dry run that reports which tool/recipe would be chosen without building anything. Returns a note on how the description was interpreted plus the underlying builder's result (created nodes, exposed controls, and a preview image).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
descriptionYesNatural-language description of the visual system.
parent_pathNoParent COMP path the generated visual-system container is created inside./project1
resolutionNoAdvisory target resolution. Recorded in the build note; the sub-builders use their own internal sizes and do not enforce this per-node.1080p
target_fpsNoAdvisory target frame rate (informational only — TD's real cook rate is a project-level setting, not set here).
Behavior4/5

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

Annotations already indicate it's not read-only or destructive. Description adds detail: it classifies intent, delegates, creates COMP, verifies, previews, returns interpretation and builder result. No contradictions.

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?

Concise 5-sentence description that front-loads purpose, explains process, compares to sibling, and states return value. No wasted words.

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

Completeness5/5

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

Given 4 parameters (1 required), no output schema, but description fully explains behavior, return value, and relation to sibling. No gaps.

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. Description does not add extra meaning beyond what the schema already specifies for each parameter.

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?

Clearly states it creates a complete visual system from natural language. Distinguishes from sibling 'plan_visual' by specifying that this tool actually builds, while plan_visual is a dry run.

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

Usage Guidelines5/5

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

Explicitly provides when to use this tool (to create) and when not to (use plan_visual for dry run). No ambiguity.

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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