Dali by Lulu

score_creative

Read-only

Score an actual ad IMAGE (not the text prompt) for conversion — before you spend.

Conversion lives in the pixels, so this scores the real creative and gives you ONE answer combining two views, in a single call: • HEADLINE score = how much it visually resembles PROVEN WINNERS (Vertex embedding vs the live winner corpus). The sharpest predictor — it reads the whole look and self-solves archetype (a premium ad resembles premium winners, not scammy direct-response ones). • WHAT TO CHANGE = the specific winning attributes it's missing (Gemini vision vs category priors) — the actionable detail. • DEFECT GATE = generation defects (extra fingers, garbled text, warped anatomy).

Use it on a generated image, a mockup, or any ad you're about to run.

Returns: score — 0-100 headline: visual similarity to proven winners verdict — one-line looks-like-a-winner / partial / rework call looks_like — the real proven winners it resembles (advertiser, category, days-run) what_to_change — high-lift winning attributes it lacks, each with a fix sentence you_already_have — winning attributes it already has has_defect/defects — generation defects to fix before shipping detail — raw numbers {embedding_score, attribute_score} for transparency

category examples: beauty, supplements, wellness, fitness, food, apparel, tech, pets. Leave category empty for a cross-vertical look-alike match + defect QA.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`category`	No
`image_url`	Yes

Output Schema

TableJSON Schema

Name	Required	Description	Default
No arguments

Schema Changelog

Changes observed during successful MCP inspections.

AddedJul 12, 2026

TDQS

A4.6/5.0

Behavior5/5

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

The description details the internal mechanics (Vertex embedding vs winner corpus, Gemini vision vs category priors, defect gate) and explains the meaning of scores and other outputs. This goes beyond the readOnlyHint annotation, which is consistent with the descriptive analysis.

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?

The description is well-structured with a clear header, bullet points for return fields, and usage examples. It is relatively lengthy but every sentence adds value, and the structure aids readability.

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?

The description covers all return fields mentioned, including edge cases (empty category), and provides context on how the tool works. Given the complexity and the presence of an output schema, the description is sufficiently complete.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The description explains the category parameter with examples and the effect of leaving it empty, adding significant value beyond the schema. The image_url parameter is self-explanatory, and overall the description compensates for the 0% schema description coverage.

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 tool scores an ad image for conversion, using visual similarity to proven winners. It specifies the resource (ad image) and the action (score), and distinguishes from sibling tools like score_prompt which scores text prompts.

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 guidance on when to use the tool ('before you spend', on generated images, mockups, or any ad about to run) and offers concrete examples for the category parameter. It lacks explicit comparison with all siblings, but the context is sufficient.

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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