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Dali by Lulu MCP Server

answering

Dali by Lulu is answering right now. Last checked 12 min ago. 303 installs a week from pypi. It exposes 16 tools. Last commit 13 Jul 2026.

The prediction MCP — score your prompt before you generate, so you never waste a credit.

Installs per day peak 240 · avg 58 · +5% w/w
a month agotoday
Uptime history 47 days of history · worst day 99%
47 days agonow
98.9%
Uptime 24h
90 of 91 checks
16
Tools
read from the server
340 ms
Response time
average over 24h
303
Installs / week
npm and PyPI

What the code does

We read the source, 21 h ago · tools taken from the live server · rules 3dff92dd89df

Capabilities

What this server is able to do. For an MCP server this is often the job itself — a terminal server runs commands because that is what it is for. Listed so you know what you are plugging in, not as an accusation.

const child = spawn(process.execPath, [mcpRemoteEntry, url, ...process.argv.slice(2)], {

Is this your server and something here is wrong? Tell us — corrections are free and do not require a plan.

This code can reach further than it looks

We found places where it runs commands, builds paths or queries from values it is given. None of that is a flaw by itself — it becomes one when the code changes, and code changes quietly between releases. We re-read it on every one.

Three servers free · no card

Connect this server

Endpoint below is the one we actually reach during checks — not the one copied from a README. Last verified 12 min ago.

run in your terminal
claude mcp add dali --transport http https://dali.getlulu.dev/mcp
~/Library/Application Support/Claude/claude_desktop_config.json
{
  "mcpServers": {
    "dali": {
      "url": "https://dali.getlulu.dev/mcp"
    }
  }
}
~/.codex/config.toml
[mcp_servers.dali]
url = "https://dali.getlulu.dev/mcp"
.cursor/mcp.json
{
  "mcpServers": {
    "dali": {
      "url": "https://dali.getlulu.dev/mcp"
    }
  }
}
.vscode/mcp.json
{
  "mcpServers": {
    "dali": {
      "url": "https://dali.getlulu.dev/mcp"
    }
  }
}

Available tools 16

Read directly from the server with tools/list, grouped by what they act on. If a tool disappears, we record the date.

score
score_creative
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.
score_creative_from_view
Score an ad creative YOU are looking at (e.g. a pasted/attached image) against the winning corpus — no URL needed. Use this when the user shares an image in the conversation: read the creative yourself and fill in what you see, and Dali scores it against what wins in the category (3,800+ proven winners), returning the conversion verdict and exactly which winning attributes it's missing. You (the model) provide the visual read; Dali provides the winning-data scoring. (For a fetchable image URL, prefer score_creative — it adds the embedding similarity headline, which needs the real pixels.) Fill these from looking at the image: category — vertical: beauty, wellness, supplements, fitness, food, apparel, tech, pets lighting — warm lighting | natural light | studio light | dramatic lighting | clinical bright | dark moody | neon subject — single person | group | product only | no person | before after subject_age — young adult | middle age | senior | child | none format — ugc selfie | testimonial | product hero | lifestyle | chart infographic | text meme | comparison text_density — none | light | heavy dominant_emotion — calm | excited | trust | fear | aspiration | neutral eye_contact — true if a person looks at camera offer_visible — true if a price/discount/offer is shown defects — list any generation defects (extra fingers, garbled text, warped anatomy); [] if clean Returns: conversion_score (0-100), verdict, matched (winning attributes it has), missing (high-lift attributes to add, each with a fix sentence), has_defect/defects.
score_prompt
Score a prompt for a generation target (0–100) and, if it's weak, return the rewrite brief — in ONE call. Reads intent with a fast heuristic keyword analyzer, scores the prompt, then: • score ≥ 70 (A/B) → returns the scorecard and tells you to proceed. • score < 70 (C/D/F) → returns the scorecard PLUS a rewrite brief so you can fix it without a second call. Write the enhanced prompt from the brief, then call track_enhancement(original, enhanced, generator). Returns a ScoreCard (overall, grade A–F, per-dimension breakdown, what's missing, anti-patterns, verdict) plus needs_enhancement, and enhancement_brief when weak. category (optional): the ad vertical (e.g. "wellness", "beauty") — when set and conversion priors exist, the brief upgrades to a conversion-justified rewrite. Supported generators: veo3, higgsfield, midjourney, flux, kling, sora, imagen…
score_variations
Score 2–8 prompt variations for the same generator and rank them best-to-worst. Use this when you've drafted multiple versions of a prompt and want to pick the winner without burning generation credits. Returns a ranked list with per-dimension comparison so you can see exactly why one variant beats another.
analyze
analyze_intent
Parse a creative prompt into structured intent dimensions.
analyze_winning_formula
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)
community
community_benchmark
Compare your prompt against community top scorers for this generator. Returns your score, missing A-grade patterns, and highest-ROI patterns to add.
creative
creative_patterns
Community graph: which patterns consistently produce high-grade prompts for this generator? Powered by the V3 graph brain (Supabase PostgreSQL). Every scored prompt contributes. Returns top patterns by type, enhancement unlocks, and cross-model universal patterns.
dali
dali_version
Current Dali MCP version and changelog. Check this whenever you want to know what tools are available, what changed in the latest release, or which version is running.
enhance
enhance_prompt
Get a rewrite brief for this prompt + generator. YOU write the enhanced prompt from the brief. Returns a structured brief with score_before, rewrite_brief, and llm_instructions. category (optional): the ad vertical (e.g. "wellness", "beauty") if known. When set and conversion priors exist for it, the brief upgrades from craft advice to a conversion-justified one, backed by real ad-performance data. IMPORTANT: After you write the enhanced prompt, you MUST call track_enhancement(original_prompt, your_enhanced_prompt, generator) immediately. This is not optional — it records the improvement and is required for the graph to learn.
enhancement
enhancement_path
Show the most reliable path from a bad grade to an A on this generator. Mines the Dali graph for all F/D → A/B enhancement pairs and surfaces the patterns that appear most consistently in the 'after' side. These are the highest-ROI moves for this specific generator. Use this when: - A prompt just scored D or F and you're not sure what to fix - You want to know which improvements matter most for a specific generator - You want to understand generator-specific enhancement strategy
generators
list_generators
List all supported generation targets (providers + models) with medium and core strength.
prompt
prompt_neighbors
Find community A/B-grade prompts structurally similar to yours. Uses graph traversal (Memgraph) to locate prompts that share the most creative patterns with your input and scored A or B on the same generator. Returns what those prompts did right — so you can adopt the same moves. Use this when: - Your prompt scored C or below and you want inspiration - You want to see how the community solved the same creative problem - You need concrete A-grade examples, not abstract advice
story
my_story
Your Dali creative report — scoring history, generator stats, recent scorers, creative DNA.
suggest
suggest_generator
Recommend the best generator for your creative concept and per-generation budget. Analyzes the concept's creative signals (motion, style, subject type, use case) and matches them to generators within your budget. Returns a ranked list so you can make an informed choice before scoring the actual prompt.
track
track_enhancement
Record an enhancement pair in the Dali graph brain. Call this AFTER you write an enhanced prompt from score_prompt's brief or enhance_prompt. This records the before→after improvement so the graph learns which rewrites consistently push scores up — enriching creative_patterns and community_benchmark over time. Returns before/after scores so you can confirm the delta.

Endpoints

URLTransportStateLatencyChecked
https://dali.getlulu.dev/mcp streamable-http answering 216 ms 12 min ago

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Dali by Lulu — questions

Answers built from our own checks of this server.

What can Dali by Lulu do?
It exposes 16 tools, read directly from the server on our last check. Among them: analyze_intent, analyze_winning_formula, community_benchmark, creative_patterns, dali_version, enhancement_path and 10 more. The full list with descriptions is on this page — we take it from the server itself via tools/list, not from a README. How MCP servers expose tools in the first place →
What is Dali by Lulu mostly used for?
Its tools cluster around score and analyze. That is what this server is built to work with — the grouping comes from the actual tool names, not from a category we assigned.
Is Dali by Lulu working right now?
We send a real MCP handshake every 15 minutes. Over the last 24 hours 90 of 91 checks got a reply (98.9%), average response time 340 ms. The bar chart above shows every period we have measured.
How do I connect Dali by Lulu?
Copy the ready config from this page — we generate it for Claude Code, Claude Desktop, Codex, Cursor and VS Code, each with the file path that client actually reads. It is a remote server, so there is nothing to install — the client connects to the address.
Does Dali by Lulu need an API key?
No. Dali by Lulu completed a full MCP handshake with us as an anonymous client and listed its tools without asking for anything. All 16 of them are readable on this page. This is what we observed, not what the docs claim.
How fast is Dali by Lulu?
It answers our handshake in 340 ms on average, which is faster than 45% of all working MCP servers we measure. The comparison comes from our own checks across the whole registry, every 15 minutes.
How many people use Dali by Lulu?
The pypi package dali-mcp was installed 303 times in the last week. Week over week that is +5%. We show installs rather than GitHub stars on purpose: a star is a bookmark, an install is someone actually running it.
Is Dali by Lulu open source?
Yes — it is published under the MIT licence, written in Python and 9 stars on GitHub. The source link is on this page, so you can read exactly what it does with your data before you connect it.