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delete_paint_lab_trained_style

Read-only

Delete one of your trained Paint Lab styles (ready or failed). A style that is still training cannot be deleted yet. Does not charge or refund. Results already made in that style are kept.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
styleIdYesTrained style id from train_paint_lab_style or list_paint_lab_trained_styles.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
deletedYesTrue when the style was deleted.
styleIdNoId of the deleted style.

Schema Changelog

Changes observed during successful MCP inspections.

  1. Added

TDQS

A3.6/5.0
Behavior1/5

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

The description announces a destructive operation ('Delete') while the annotations declare readOnlyHint=true and destructiveHint=false. That is a direct, serious inconsistency between description and structured metadata. Although the description does add genuine side-effect context (no charge or refund, existing results preserved), the contradiction with the annotations forces the floor score.

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, zero filler: scope first, then precondition, then cost behavior, then retention behavior. Each sentence carries distinct, decision-relevant information.

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?

For a one-parameter delete with an output schema, the description covers preconditions, billing impact, and what is retained, which is close to complete. The one thing it fails to resolve is the conflict with its own annotations, which is why it is not a 5.

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% and the single styleId parameter is already documented in the schema, including where to obtain the id. The description adds no format or sourcing detail beyond that, so the baseline 3 applies.

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?

Specific verb+resource ('Delete ... trained Paint Lab styles') with an explicit scope qualifier ('ready or failed'). An agent can distinguish it immediately from the nearby siblings get_paint_lab_trained_style, rename_paint_lab_trained_style, and train_paint_lab_style.

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?

Gives a clear precondition ('A style that is still training cannot be deleted yet'), which tells the agent when this call will fail. It does not explicitly route to a sibling (e.g., list_paint_lab_trained_styles to find a deletable id), so it stops short of full when/when-not/alternatives guidance.

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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