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reconcile_video_lab_generation

Read-only

Refresh a Video Lab job from the provider. Does not charge again.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
videoIdYesVideo Lab video id to refresh from the provider. Does not charge again.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
idNoRow id. Some list tools use id instead of generationId.
statusNoJob status such as pending, queued, in_progress, completed, or failed.
videoIdNoLibrary clip id for this job, when one exists.
videoUrlNoPlayback URL when the job has finished.
createdAtNoISO timestamp when this job was created.
modelSlugNoCatalog model slug from the matching list_*_models tool.
outputUrlNoDownload or playback URL when the job has finished.
generationIdNoGeneration id. Poll the matching get_* tool until status is completed or failed.
creditsChargedNoUsage already consumed from this month’s generation allowance (internal units).

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A3.5/5.0
Behavior3/5

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

Annotations already declare readOnlyHint=true and destructiveHint=false, so the safety profile is covered. The description adds the billing-relevant trait 'Does not charge again' and the external-provider dependency, but it does not explain what 'refresh' does (e.g., whether it updates local state or returns latest status) beyond what a reader would infer.

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 short sentences with no filler; the core action is front-loaded and the non-charge note is a useful qualifier. It is well-sized for a single-parameter tool.

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?

The output schema and annotations cover return values and read-only safety, so the description only needs to convey the operation's purpose. However, it leaves unclear how this tool relates to get_video_lab_generation or when a refresh is necessary, which an agent would need for confident selection.

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 coverage is 100%, so the input schema fully documents the videoId parameter. The description and the schema field repeat the same 'refresh from provider / does not charge again' information, so the description adds no additional parameter meaning beyond the schema.

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 action ('Refresh') on a clear resource ('a Video Lab job') and notes that it does not re-charge, distinguishing it from generation/quote tools. It does not explicitly name sibling alternatives like get_video_lab_generation, so the differentiation is implied rather than stated.

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 implies this tool is for existing Video Lab jobs by saying 'refresh from the provider' and 'does not charge again,' which signals it is not for creation. It does not provide explicit when-to-use guidance or contrast with get_video_lab_generation or generate_video_lab, leaving the choice to inference.

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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