Dali by Lulu

analyze_winning_formula

Read-only

Find YOUR winning ad formula from your own numbers — paste your ads export.

The category prior is a cold-start fallback; the real signal is what wins in YOUR account. Paste an ads CSV (a creative image-URL column + a performance column — CPA / CTR / ROAS / purchases) and Dali runs vision on your winners vs losers and returns the attributes that separate them, plus how your account compares to the industry median.

If an email is supplied, the formula is saved and emailed with a ready-to-paste Claude prompt wired to Dali — so scoring the next creative is one step.

Returns: formula — attributes over-represented in your winners (value, winner%/loser%, lift) benchmark — your median vs the vertical's industry median (when category given) analyzed — how many winners/losers were read, and the metric direction saved — whether the lead+formula were captured (only when email supplied)

Input Schema

TableJSON Schema

Name	Required	Description	Default
`csv`	Yes
`email`	No
`category`	No

Output Schema

TableJSON Schema

Name	Required	Description	Default
No arguments

Schema Changelog

Changes observed during successful MCP inspections.

AddedJul 12, 2026

TDQS

A4.5/5.0

Behavior4/5

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

Annotations declare readOnlyHint=true, which aligns with the analysis nature. The description goes beyond by detailing outputs (formula, benchmark, analyzed, saved) and conditions (email supplied). No contradictions.

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 front-loaded with a clear purpose and structured into paragraphs. It is informative but slightly verbose in the middle section; still earns its space.

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?

Covers inputs, process, outputs, and conditions comprehensively. Output schema existence complements the description; no missing aspects.

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

Parameters5/5

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

With 0% schema description coverage, the description fully explains each parameter: csv (ads export with specific columns), email (for saving/emailing), category (for benchmark). Adds meaning beyond names.

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 identifies winning ad formulas from user data. It uses specific verbs ('Find', 'analyzes') and distinguishes from sibling tools like score_creative by requiring a CSV export.

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 guidance on when to use: when you have an ads CSV and want to uncover winning attributes. It hints at alternatives (cold-start fallback) but does not explicitly exclude other tools.

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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