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Create growth system

create_growth_system

Construct a procedural L-system vine or tree by iterating a grammar and walking the result as a 3D turtle, then thicken and render the polyline.

Instructions

Build an L-system / vine-growth generator: a Script SOP iterates a context-free rewriting grammar from axiom for generations steps, then walks the resulting string as a 3D turtle to draw a polyline tree. Recognised symbols: F (forward draw), f (forward no draw), + - (yaw ± branchAngle), & ^ (pitch), \ / (roll), [ ] (push/pop state). Other symbols are no-op constants (use X/A/B as grammar variables that expand but don't draw). Multiple rules sharing a from symbol trigger weighted-random stochastic selection (weight defaults to 1; seed controls the RNG). The polyline tree is thickened with a Tube SOP, recentred, and rendered. Complements create_particle_flock (boids) and create_gpu_particle_field (curl-noise) as the deterministic CPU-geometry idiom. Returns a summary plus a JSON block with the container path, output path, rules DAT path, exposed controls, errors, warnings, and an inline preview.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
nameNoContainer baseCOMP name.growth_system
parentNoParent network where the container is created./project1
rulesNoContext-free rewriting rules. Multiple rules sharing the same `from` symbol trigger weighted-random stochastic choice (weight defaults to 1).
generationsNoRewrite iterations. Capped at 7 because string length grows ~k^n and freezes the SOP cook.
axiomNoInitial string before rewriting.F
branchAngleNoTurtle turn angle (degrees) for + / - / & / ^ / \ / / symbols.
step_lengthNoWorld units per F stroke.
thicknessNoTube SOP radius for the rendered branches.
colorNoConstant MAT colour (RGB, 0..1).
seedNoRNG seed for stochastic rule selection.
expose_controlsNoExpose Generations / BranchAngle / StepLength / Thickness on the container.
Behavior5/5

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

The description goes beyond annotations to detail key behaviors: generation capped at 7, stochastic rule selection with seed control, the full symbol set, and output format including a JSON block with preview. No contradiction with annotations.

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 front-loaded with the main purpose and is well-structured, but it is somewhat lengthy. Every sentence is substantive, but could be slightly tightened without losing information.

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 11 parameters, high complexity, and no output schema, the description comprehensively covers the generative process, symbol set, rule semantics, output format, and behavioral constraints. It provides everything an agent needs to invoke the tool correctly.

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

Parameters5/5

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

Despite 100% schema coverage, the description adds significant context beyond the schema: explains the L-system symbols (F, +, -, etc.), the weighted-random stochastic mechanism, the cap on generations, and the purpose of each parameter (e.g., branchAngle used for multiple symbols). This greatly aids agent understanding.

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 tool builds an L-system/vine-growth generator using a Script SOP with a rewriting grammar and 3D turtle walk. It distinguishes itself from siblings by calling it the 'deterministic CPU-geometry idiom' versus create_particle_flock and create_gpu_particle_field.

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 explicitly names complementary tools and categorizes this as a deterministic CPU-geometry approach, helping the agent choose between alternatives. However, it doesn't provide explicit when-not-to-use guidance, such as cases where GPU-based approaches would be preferable.

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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