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Execute TD Brain Plan

td_brain_execute
Destructive

Execute a BrainPlan for TouchDesigner with transactional validation, rollback on failure, and optional local learning.

Instructions

DEFAULT apply path for a BrainPlan: use this when you already have a BrainPlan from td_brain_plan (or a draft from td_brain_propose) and need TDPilot to apply it transactionally with validation, rollback, and optional local learning. Prefer the lower-level td_transaction_apply only when you hold a ready PatchPlan/BrainPlan and want to drive raw transaction controls yourself (no grounding, no learning). Do not use the legacy td_plan_patch / td_patch_* pipeline for new builds.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
planNoBrainPlan dict returned by td_brain_plan. Raw free text is not accepted here. Omit when passing plan_id instead.
plan_idNoID of the most recent td_brain_plan result. Server-side lookup — avoids echoing the full multi-KB plan back through the host context window (and the silent-corruption risk of hosts that re-serialize large tool arguments). Provide exactly one of plan or plan_id.
repair_budgetNoOptional bounded auto-repair attempts. Omitted preserves legacy zero-repair behavior.
learn_on_successNoPersist a compact validated task trace to td_knowledge_* memory.
transaction_policyNo'rollback_on_failure' (default), 'dry_run', or 'no_rollback'.rollback_on_failure
confirm_visual_payloadNoReserved for future image payload confirmation; currently no large images returned.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Behavior4/5

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

Annotations already disclose destructive and non-idempotent behavior. The description adds context about transactional apply, validation, rollback, optional learning, and bounded auto-repair via repair_budget. It does not contradict annotations and adds meaningful behavioral details beyond structured fields.

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?

Two sentences with no wasted words. The first sentence front-loads purpose and key behaviors; the second provides critical usage guidance against alternatives. Every sentence earns its place.

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?

Covers core use case, alternative tools, transactionality, validation, rollback, learning, repair budget, and plan_id optimization. Minor gap: does not clarify behavior when both plan and plan_id are omitted (though schema defaults handle it). Overall, it is very complete for a complex tool with 6 parameters and existing schema descriptions.

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

Parameters4/5

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

Schema coverage is 100% with descriptive param descriptions, providing a baseline of 3. The description adds value by explaining the rationale for plan_id (avoiding echoing large plans back) and implicitly covering repair_budget and learn_on_success through 'optional local learning' and 'bounded auto-repair attempts'.

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 explicitly states the tool as 'DEFAULT apply path for a BrainPlan' and describes it as applying transactionally with validation, rollback, and learning. It clearly distinguishes from sibling tools like td_transaction_apply and the legacy pipeline.

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?

Provides clear guidance: use when you have a BrainPlan from td_brain_plan or draft from td_brain_propose. Explicitly states when to prefer the alternative td_transaction_apply (for raw controls without grounding/learning) and warns not to use legacy td_plan_patch pipeline.

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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