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gen_tripo_wait_for_task

Wait for a Tripo generation task to reach its final state by polling until completion or timeout.

Instructions

Poll a Tripo task until it reaches a finalized status or timeout.

KB: see knowledge_base/31_GENERATIVE_CONTENT_PIPELINE.md#tripo-task-family Example: gen_tripo_wait_for_task(task_id="model-task-id", timeout_s=900, poll_s=10)

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
poll_sNo
task_idYes
timeout_sNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.0.0

TDQS

B3.2/5.0
Behavior3/5

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It does reveal the key blocking trait — that the tool polls until a finalized status or timeout — which is essential for an agent to know before calling. However, it does not clarify what happens on timeout (error vs. partial result), what a 'finalized status' means, or the polling cadence semantics, leaving important behavioral gaps.

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 efficient: one purpose sentence, a KB pointer, and a concrete example. Every element earns its place and the example is genuinely instructive. Not bloated, though the KB reference could arguably be shortened.

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?

For a three-parameter polling tool with an output schema and zero annotation coverage, the description is mostly adequate — it states the purpose and shows parameter usage. Gaps remain around timeout behavior, return-value semantics, and what constitutes a 'finalized' status, but the output schema offsets some of this. Reasonably complete for a simple poller.

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 0%, so the schema offers only names, types, and defaults. The description's example (task_id='model-task-id', timeout_s=900, poll_s=10) demonstrates realistic usage and implies the units (seconds) and role of each parameter. This partially compensates for the missing schema descriptions but doesn't explicitly explain each parameter's semantics or edge cases.

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 states a specific verb and resource ('Poll a Tripo task') with a clear termination condition ('until it reaches a finalized status or timeout'). This distinguishes it from siblings like gen_tripo_get_task_status (one-shot status check) and gen_tripo_download_result (retrieval). It doesn't explicitly name a sibling it differs from, but the purpose is unambiguous.

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 guidance on when to use this tool versus alternatives such as gen_tripo_get_task_status, or when to skip polling and go straight to download. The KB reference could hold this context but the description itself provides no usage context, exclusions, or sequencing advice beyond a bare example.

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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