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quote_paint_lab_style_training

Read-only

Quote training your own Paint Lab style from 8–20 of your own paintings. Does not train or charge. Returns quoteId and the job facts line. Quotes expire after about 10 minutes. You can keep up to 12 styles; delete one to train another.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
nameYesStyle name, 1–40 characters (for example "Ink and wash").
imagesYes8–20 different paintings you own in Paint Lab. Pick the ones that look most like your style; more variety teaches it better.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
nameNoStyle name this quote locked in.
factsNoJob facts line for this training, for example "12 drawings · Your style".
stepsNoTraining steps (fixed).
quoteIdYesQuote id. Pass this to the matching generate tool. Quotes expire.
expiresAtNoISO timestamp when this quote expires.
imageCountNoHow many drawings this quote covers.
pricingVersionNoPricing version used for this quote.
requiredCreditsNoUsage this quote would consume from this month’s generation allowance (internal units).

Schema Changelog

Changes observed during successful MCP inspections.

  1. Added

TDQS

A4.5/5.0
Behavior5/5

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

Goes well beyond the readOnlyHint/destructiveHint=false annotations by disclosing that no charge occurs, that a quoteId and job facts line are returned, that quotes expire in ~10 minutes, and that a 12-style capacity cap exists. Expiration and capacity are operationally critical traits not captured by any structured field.

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?

Four short sentences, front-loaded with the core purpose followed by constraints. No filler; every clause (no charge, return value, expiry, capacity) carries distinct operational value.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a quoting tool with an output schema already documenting returns, the description supplies the missing behavioral context an agent needs: cost behavior, expiry window, and style-capacity limits. Nothing required to call it correctly is absent.

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 100%, so the schema already documents both the name and images parameters in detail (including the 8–20 range and layerId semantics). The description only echoes the 8–20 painting count, adding no syntax or format meaning beyond the schema.

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?

States a specific verb and resource (quote Paint Lab style training) plus the scope (8–20 of your own paintings), and explicitly disambiguates from the sibling that actually trains by saying 'Does not train or charge.' An agent can distinguish this from train_paint_lab_style and quote_paint_lab without opening a schema.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The 'Does not train or charge' clause signals this is a preliminary, non-committal step, and the capacity note ('keep up to 12 styles; delete one to train another') hints at the delete_paint_lab_trained_style workflow. However, it never explicitly states 'use this before train_paint_lab_style,' leaving the ordering 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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