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Compare a render with the SKU reference (visual QC)

vivid_compare_product

Compares a generated or retouched product image with the real SKU photo to verify shape, colors, materials, logos, and details, returning a match score, verdict, differences, and fix prompt.

Instructions

Visual quality control: checks that the product in a generated/retouched image matches the real product photo (SKU card). Vision LLM compares shape, proportions, materials/finish, colours, stones/elements, logos, hardware — product only, background ignored. Returns match 0–1, verdict pass|review|fail, a list of differences with severity, a summary and a fixPrompt you can pass straight to vivid_retouch. 1 credit per call (a retry is charged again), ~15–25 s.

Input Schema

NameRequiredDescriptionDefault
focusNoWhich object to compare when the candidate shows several, e.g. "the ring on the hand".
languageNoLanguage of the summary.it
candidateYesImage to check: asset id, URL or local path.
referenceYesSKU / real product photo: asset id, URL or local path.
skuDescriptionNoSKU notes: material, stone, colour, size… anything the reference photo does not show.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.36.0

TDQS

A4.4/5.0
Behavior5/5

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

Goes well beyond the annotations by disclosing cost (1 credit per call, retries charged again), latency (~15–25 s), and the non-idempotent billing implication, which is exactly the kind of context the readOnlyHint/idempotentHint flags cannot convey. It also describes the comparison dimensions and the return payload.

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?

Front-loaded with the purpose and packed with high-value facts, with no filler sentences. It is a single dense run-on paragraph, which slightly hurts scannability of the cost/latency and return-value details.

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?

No output schema exists, yet the description enumerates the return shape (match 0–1, verdict pass|review|fail, differences with severity, summary, fixPrompt), plus cost and timing. For a 5-parameter vision comparison tool, an agent has everything needed to call and interpret it.

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 coverage is 100%, so candidate, reference, focus, language and skuDescription are already documented in the schema. The description adds the useful constraint that only the product is compared and that skuDescription covers what the reference photo cannot show, but it does not add syntax or format detail beyond the schema. 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?

States a specific verb and resource ('checks that the product in a generated/retouched image matches the real product photo') and scopes it to product-only comparison with background ignored. This clearly separates it from siblings like vivid_analyze_product and vivid_retouch.

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 clear context: use it as visual QC on a generated/retouched candidate against a SKU reference, and it names the downstream consumer of fixPrompt (vivid_retouch). It does not explicitly contrast with vivid_analyze_product, so the alternative-selection guidance is implied rather than stated.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.