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Propose Host-Authored TD Brain Draft

td_brain_propose
Read-only

Validate a draft candidate graph into an executable BrainPlan. Accepts drafts matching the authoring contract and returns ready-to-run plans or machine-readable rejections for fixes.

Instructions

Use this when you have authored a draft candidate graph from a td_brain_ground grounding pack and need TDPilot to validate it into an executable BrainPlan. It is read-only and never mutates TouchDesigner: accepted drafts are compiled, gated by parameter semantics, and cached server-side so td_brain_execute(plan_id=...) can run them immediately; rejected drafts return machine-readable rejections to fix and retry.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
draftYesHost-authored draft candidate graph matching the td_brain_ground authoring_contract draft_schema (label, concepts, edges, required_ops, ...).
intentNoOriginal natural-language intent behind the draft. Defaults to the draft label so the same intent used for td_brain_ground can be carried through.
target_rootNoAbsolute TD parent/root path the plan will build inside./project1
detail_levelNoResponse detail: 'full' (compatibility default) or 'summary'.full
grounding_idNoOptional grounding_id from td_brain_ground; binds review to the original task facets.
validation_profileNoValidation profile. 'auto' resolves to structural_visual_safe.auto
draft_schema_versionNoDraft contract version: '2' (default) or legacy '1'.2

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Behavior5/5

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

Discloses caching, acceptance/rejection behaviors, and server-side compilation beyond annotations; no contradictions with readOnlyHint=true.

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, front-loaded with purpose and usage, no redundancy or fluff.

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?

Describes pipeline and acceptance behavior; output schema covers return values. Could mention validation profile details but overall sufficient.

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% so baseline is 3; description adds workflow context for draft and default intent, justifying one extra point.

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?

Description clearly states the tool validates a draft candidate graph into an executable BrainPlan, distinguishing it from siblings like td_brain_ground and td_brain_execute by its specific pipeline role.

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 says when to use ('after grounding, before execute'), and notes it is read-only, guiding against mutation use cases.

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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