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Style Profile

style_profile
Idempotent

Integrated styling verdict: seasonal color (measured colorimetry consensus) + face-impression zone (published geometry rules) + body frame + integration rules (palette subset, taste, silhouette, modifiers). 1-3 face photos, optional full-body + questionnaire. Flat $0.05/call.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
styleNoOne of: feminine, masculine, neutral. Styling context: feminine, masculine or neutralneutral
imagesNoUp to 3 objects {image_url | image_base64}
image_urlNoAbsolute http(s) URL of the image (JPEG/PNG/WebP)
x_paymentNoOptional: base64 X-PAYMENT header value (x402 exact scheme, USDC on Base mainnet). Omit it to receive the payment requirements (accepts[] + docs) for this call without being charged.
image_base64NoBase64-encoded image bytes (alternative to image_url)
questionnaireNoOptional frame questionnaire answers (see /catalog input spec)
body_image_urlNoOptional full-body photo URL for frame estimation
body_image_base64NoSee the product's input spec in /catalog

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
bodyYesJSON body of the underlying call (see /samples/{product} for each product's shape)
statusYesHTTP status of the underlying call (200 result, 402 payment terms, 4xx input error)
how_to_payNo
receipt_idNoset on paid results; free re-fetch via get_receipt
payment_requiredNotrue when status is 402: body holds x402 payment terms, not a result. Pay and call again with x_payment

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A3.7/5.0
Behavior4/5

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

Annotations already cover the safety profile (idempotent, non-destructive, open-world), so the bar is lower. The description still adds genuinely useful behavioral context beyond them: the flat $0.05/call cost and the photo-count bound. It does not disclose output shape, but an output schema exists to carry that.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Two dense sentences that front-load the verdict composition before inputs and pricing. The parenthetical qualifiers are information-rich rather than filler, though the first sentence is heavy enough to slow parsing.

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 an 8-parameter, multi-photo tool with a nested questionnaire and an output schema, the description covers the essential inputs and cost, and the schema handles payment mechanics and return values. Only a brief note on the payment-requirement flow would make it fully self-contained.

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 parameters are already documented. The description's '1-3 face photos, optional full-body + questionnaire' only restates what the schema says and adds no syntax or format detail beyond it, so the baseline 3 applies.

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 names a concrete deliverable (a combined seasonal-color + face-impression + body-frame styling verdict) with the four component rule sets spelled out, so an agent knows what it produces. It also implicitly distinguishes itself from portrait_analysis by being 'integrated', but it never explicitly contrasts with the sibling style_report, leaving some ambiguity.

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?

It states the input shape (1-3 face photos, optional full-body + questionnaire) and the price, which implies when the tool is applicable. However, it gives no explicit when-to-use vs alternatives or when-not guidance relative to siblings like style_report or portrait_analysis.

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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