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spatial_prepare_tripo_generation_batch

Plans a Tripo generation batch for spatial compositions, reconciling text and image crop inputs, confirming spend, and outputting import, binding, placement, and validation follow-ups.

Instructions

Prepare a guarded Tripo generation batch for spatial compositions.

KB: see knowledge_base/10_WORLD_BUILDING.md#9-world-building-best-practices

This local planner accepts an interior composition, screenshot reconstruction output, or generated-asset binding output. It reconciles text and crop-based Tripo handoffs, keeps spend confirmation explicit, and returns the import, binding, placement, validation, and iteration follow-ups needed to finish the worldbuilding loop.

Example: spatial_prepare_tripo_generation_batch(composition_plan_json="")

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNo
content_pathNo
session_nameNo
confirm_spendNo
prefer_image_cropsNo
composition_plan_jsonYes
include_text_fallbacksNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.0.0

TDQS

B3.1/5.0
Behavior2/5

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It says it 'keeps spend confirmation explicit' and is 'guarded', but it does not clarify whether this tool itself makes external calls, spends credits, or mutates state. It does not describe side effects, rate limits, or whether it is read-only. The term 'returns follow-ups' suggests a planning step, but the description is vague about what happens to the input and what the tool actually does beyond preparing a batch.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness3/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is reasonably concise but includes a KB reference line and an example, which add some overhead. The core explanation is about 3 sentences, but it could be tightened by integrating the example into the text. It is not overly verbose, but it is not as lean as it could be.

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 tool's complexity (7 parameters, only 1 required, and multiple input types), the description is incomplete. It does not explain the purpose of the optional parameters, nor how to choose among the three accepted input types. While an output schema exists, the description's mention of follow-ups is vague and does not clarify what the tool returns in terms of the workflow. It fails to provide enough context for an agent to correctly configure the call.

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

Parameters2/5

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

The schema has 0% description coverage for its 7 parameters, and the description does not compensate. Only composition_plan_json is shown in an example, and the other six parameters (limit, content_path, session_name, confirm_spend, prefer_image_crops, include_text_fallbacks) are never explained. The description adds no meaning about what these parameters control or how they relate to the tool's behavior, leaving the agent to guess.

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 states a specific verb ('prepare'), a specific object ('a guarded Tripo generation batch'), and a clear domain ('for spatial compositions'). It further explains its role as a local planner that reconciles different input types and returns follow-ups, making it clearly distinct from sibling spatial planning tools like spatial_plan_interior_composition or spatial_prepare_screenshot_crop_manifest.

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 mentions it accepts an interior composition, screenshot reconstruction output, or generated-asset binding output, which gives a sense of when to use it. However, it does not explicitly state when NOT to use it, nor does it name alternative tools for different scenarios. It implies usage via input types but lacks explicit exclusions or comparisons with siblings.

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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