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Delete Custom Style

delete_user_style
DestructiveIdempotent

Delete a user style using /v1/styles/{style_id}. style_id can be either internal id or prompt_style/public_id.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
style_idYesStyle id — the internal id or the prompt_style/public id (e.g. user__mystyle_ab12).
rd_api_keyNoRetroDiffusion API key (rdpk-...) for this call only; overrides session or header auth.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

TDQS

B3.3/5.0
Behavior3/5

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

Annotations already provide destructiveHint=true and idempotentHint=true. The description adds minimal behavior context (use of specific endpoint, style_id format). It does not discuss side effects, error states, or confirmation requirements. No contradiction with annotations, but little added value beyond what annotations convey.

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?

The description is two sentences long and immediately conveys the core action. No redundant or extraneous information. Ideal conciseness for a simple delete operation.

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 tool has an output schema (presumably covering return values) and annotations that define safety. However, the description lacks context on success/failure behavior, idempotency consequences (e.g., deleting already deleted), or any follow-up actions. Adequate but not thorough for a destructive action.

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 fully documents both parameters. The description mentions style_id can be internal or public id, but this is also in the schema description. The endpoint path clue adds negligible extra meaning. Baseline 3 is appropriate since schema does the heavy lifting.

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 clearly states 'Delete a user style' with the endpoint reference. The verb 'delete' and resource 'user style' are specific and unambiguous, though it doesn't explicitly differentiate from sibling tools like 'update_user_style' or 'create_user_style'. However, the action is distinct enough given the name.

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?

The description provides no guidance on when to use this tool versus alternatives (e.g., update vs delete). There is no mention of prerequisites, irreversible consequences, or conditions under which deletion is appropriate. Instructions for when to choose delete over other actions are absent.

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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TDQS

A4/5.0
Disambiguation4/5

Most tools have clearly distinct purposes (e.g., create_inference vs start_inference_job vs get_inference_result). However, the difference between create_user_style/update_user_style and per-inference references could still cause confusion, and list_available_models/list_available_styles overlap slightly.

Naming Consistency5/5

All tool names consistently follow a verb_noun pattern (e.g., create_inference, get_balance, list_edit_tools, delete_user_style). No mixing of camelCase or other styles, making the surface highly predictable.

Tool Count5/5

With 20 tools covering authentication, inference (sync/async), styles, editing, cost estimation, and status, the count is appropriate for a pixel art generation API. Each tool addresses a distinct need without bloat.

Completeness4/5

The tool set covers the full lifecycle: auth, cost estimation, synchronous/async generation, style management, editing, and result retrieval. A minor gap is the lack of a tool to list or manage user styles (e.g., get_user_styles), but this is non-critical for core workflows.

Resources