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Moodboard → generative system

moodboard_to_system

Convert moodboard images into a matching generative system in TouchDesigner, using LLM vision or deterministic grammar to extract palette, motion, and generator choice.

Instructions

Ingest 1..6 moodboard images and build a matching generative system in TouchDesigner. Uses the vision-capable local LLM when configured to extract palette + motion + generator pick (palette hint, generator from {audio_reactive, generative_art, particle_flock, feedback_tunnel, gpu_particle_field}, optional post-FX). Falls back to a deterministic style→generator grammar otherwise. Note: preview may read 0 on a paused timeline — press Play.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
imagesYesImage paths (absolute or cwd-relative). Vault refs allowed when TDMCP_VAULT_PATH is set: e.g. 'Moodboards/foo.png'.
parent_pathNoCOMP to build the generated subsystem in./project1
styleNoHint that biases generator + post-FX choice.auto
intensityNoDrives evolution_speed / particle counts / feedback gain on the chosen generator.
includePostFxNoChain apply_post_processing with picked effects after the generator builds.
generatorNoForce a generator. 'auto' lets the LLM/grammar pick.auto
preferLlmNoWhen false, skip the LLM entirely and use the deterministic grammar.
Behavior4/5

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

Annotations indicate readOnlyHint=false, destructiveHint=false, openWorldHint=true. The description adds value by disclosing fallback from LLM to deterministic grammar, mentioning that preview may read 0 on a paused timeline (a behavioral quirk), and noting the dependency on a configured local LLM. No contradictions found.

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?

Three sentences with no fluff: first sentence states core purpose, second details the LLM/grammar dual path, third provides a practical troubleshooting note. Front-loaded and every sentence earns its place.

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?

Given 7 parameters, 2 enums, and no output schema, the description covers the workflow but omits what the output of building a generative system looks like (e.g., returned component path or success message). It assumes familiarity with TouchDesigner and does not describe side effects or prerequisites beyond annotations.

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?

Schema coverage is 100%, and parameters are well-described in the schema. The description enriches semantics by explaining how parameters like 'style' and 'generator' influence the LLM or grammar fallback, and the role of 'preferLlm' and 'includePostFx'. This goes beyond the schema's field 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 the verb 'Ingest' and 'build', the resource 'moodboard images → generative system', and the target environment 'TouchDesigner'. It distinguishes from sibling tools like 'create_generative_art' by focusing on converting moodboards into a complete system, not just a single generator.

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

Usage Guidelines3/5

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

The description implies usage when converting moodboards to generative systems but does not explicitly differentiate from sibling 'generate_from_moodboard'. It mentions fallback behavior (LLM vs deterministic) but lacks explicit when-to-use or when-not-to-use guidance compared to alternatives like individual generator creation tools.

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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