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request_illustration_revision

Spend a free revision turn on a completed illustration order. Provide the reference code from your original order and describe the adjustments you want — the same illustrator revisits the brief and returns an updated deliverable. Poll get_result with the reference code to retrieve it. Free: no payment required, the turn is included with your order.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
adjustmentsYesWhat you want the illustrator to adjust, expand, or rethink.
referenceCodeYesThe TASTE-... reference code returned when you placed the illustration order.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
tipYes
statusYes
messageYes
sessionIdYes
referenceCodeYes
turnsRemainingYes

TDQS

A4.2/5.0
Behavior4/5

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

With no annotations provided, the description carries the full burden of disclosure. It explains the free/no-payment aspect, that consuming the revision turn is included with the order, the involvement of the same illustrator, and the need to poll get_result for the result. It does not cover failure modes or state validation beyond 'completed illustration order,' but this is fairly thorough for a simple revision tool.

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, front-loaded with the primary purpose ('Spend a free revision turn'), then provides the process and retrieval instructions. The repeated mention of 'free' is slightly redundant but ensures clarity. Every sentence contributes useful information, and no unnecessary detail is included.

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?

Given the tool's simplicity and the presence of an output schema, the description adequately covers the inputs, the action, the cost (free), and how to retrieve the result via get_result. It does not mention edge cases like invalid reference codes or what happens if the order is not completed, but these are not critical for a basic revision tool. Overall, it provides enough context for an agent to use the tool correctly.

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?

The schema provides 100% coverage for both parameters: referenceCode is described as the TASTE-... reference code, and adjustments as what the illustrator should adjust. The description essentially restates these concepts ('Provide the reference code' and 'describe the adjustments you want') without adding new meaning, so it meets the baseline for schema-covered parameters.

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?

The description clearly states the action: 'Spend a free revision turn on a completed illustration order.' It identifies the resource (illustration order) and distinguishes it from sibling tools like request_think_tank_revision. The outcome is also specified: the same illustrator revisits the brief and returns an updated deliverable.

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 description provides clear context: use on completed illustration orders with a free included turn. It also instructs to poll get_result with the reference code to retrieve the updated deliverable, which guides the agent to the follow-up tool. However, it does not explicitly name alternatives or exclusions, though the illustration-specific wording and sibling tool names imply differentiation.

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

A4.2/5.0
Disambiguation5/5

Each tool targets a specific action or domain, with clear distinctions between similar ones (e.g., review_content vs prepublish_review for different review purposes, order_think_tank_session_30 vs _60 by duration). No ambiguity.

Naming Consistency5/5

All tool names follow a consistent verb_noun pattern with underscores, e.g., list_offerings, verify_certificate, request_human_approval. No mixing of conventions.

Tool Count4/5

17 tools is on the higher side but fully justified given the broad scope: expert consultations, think tanks, content reviews, verification, and human approval. Each tool serves a distinct purpose.

Completeness4/5

Covers the full workflow from discovery to ordering, revision, and on-chain verification. Minor gaps exist (e.g., no order cancellation or history listing), but core operations are well-represented.