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td_optimize_visual

Optimize visual parameters in TouchDesigner networks to achieve a specified goal using bounded parameter search.

Instructions

Autonomous visual goal optimization via bounded parameter search.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
goalYesNatural-language optimization goal.
profileNoOptional optimizer profile: balanced | complexity | motion_rhythm | stability_guard
root_pathNoRoot scope for instability checks and snapshots./project1
output_topYesTOP path used as output reference.
max_iterationsNoMax iterations.
safety_profileNoOptimizer safety profile: conservative | balanced | aggressivebalanced
snapshot_beforeNoCapture snapshot before optimization loop starts.
adjustable_paramsYesParameter search space. Each entry specifies path/param/min_val/max_val/step for a parameter the optimizer may adjust.
objective_weightsNoOptional explicit objective weights, e.g. {'motion_rhythm': 0.8, 'stability': 0.4}.
convergence_thresholdNoConvergence threshold.
param_semantics_policyNoDocs-grounded parameter safety policy for optimizer writes. 'warn' preserves bounded search with attached findings; 'block' refuses invalid or high-risk writes before mutation.warn

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior2/5

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

Annotations indicate it is not read-only and not idempotent, but not destructive. The description adds little beyond stating 'autonomous' and 'bounded parameter search.' It does not disclose that the tool may take snapshots, modify parameters, or run iterations, though these are evident from the schema. With annotations already covering basic behavior, the description should provide additional context.

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 a single sentence that is concise and gets to the point. It could be slightly expanded for clarity but is not verbose. Appropriate length.

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

Completeness2/5

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

Given the complexity (11 parameters, multiple features like snapshots, convergence), the description is too brief. It does not mention side effects, output format, or when optimization terminates. The output schema exists, so return values are covered, but behavioral completeness is lacking. Requires more context for an AI agent to use it safely.

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 tool description does not add extra meaning beyond what the schema already provides for parameters. It is adequate.

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?

The description 'Autonomous visual goal optimization via bounded parameter search' specifies the action (optimization), the object (visual goal), and the method (bounded parameter search). It distinguishes from sibling tools like td_brain_propose or td_set_param_bounds, but could be more specific about what 'autonomous' entails.

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?

No explicit guidance on when to use this tool versus alternatives such as td_brain_plan, td_brain_ground, td_set_param_bounds. The description lacks context about prerequisites or scenarios where this tool is preferred.

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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