<!-- mcp-name: io.github.Lulu-The-Narwhal/dali -->

Dali by Lulu

<p align="center"> <img src="assets/dali-lulu-scorer.png" alt="Dali by Lulu — creative intelligence MCP" width="100%"> </p>

<p align="center"> <a href="https://dali.getlulu.dev"><strong>dali.getlulu.dev</strong></a>  ·  <a href="https://dali.getlulu.dev/#install">Install</a>  ·  <a href="https://getlulu.dev">Lulu</a> </p>

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The prediction MCP that helps you avoid the AI generation tax.

Most AI generation failures are prompt failures. You can't tell the difference until after you've burned the token. Dali scores your prompt before you generate — so you never waste a credit on a bad prompt again.

You: "make a video ad for our glass serum bottle"

dali::score_prompt(prompt, "veo3")
→ 8/100  Grade: F
→ no camera move · no motion · no lighting · 8 words
→ Verdict: Generic stock footage guaranteed. Enhance first.

dali::enhance_prompt(prompt, "veo3")
→ Returns a rewrite brief — YOUR LLM writes the enhanced prompt:

  ① lead with camera — Veo 3's #1 lever: "Slow dolly", "Orbital push"
  ② describe physics: "a drop falls", "liquid ripples", "glass refracts"
  ③ lighting type + quality: "warm backlight", "rim-lit edges"
  ↳ [Camera]. [Subject + motion]. [Lighting]. [Mood]. [No text.]

✦ Claude rewrites using the brief:

  "Slow orbital push around a glass serum bottle on white marble. A single
   amber drop falls in extreme slow motion, catching warm backlight. Macro:
   liquid gold ripples outward from impact. Rim-lit edges, soft studio
   diffusion. Premium, clinical. No text."

dali::score_prompt(enhanced, "veo3")
→ 91/100  Grade: A  ✓ Safe to generate.

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Install

Hosted MCP — connect once, scores every prompt:

# Claude Code
claude mcp add --transport http dali https://dali.getlulu.dev/mcp

// Cursor / Windsurf — .cursor/mcp.json or windsurf settings
{
  "mcpServers": {
    "dali": { "url": "https://dali.getlulu.dev/mcp" }
  }
}

→ Full install guide with all clients

Self-hosted — local, no auth required:

pip install dali-mcp
claude mcp add dali -- python -m dali.server

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Tools

Tool	What it does
score_prompt(prompt, model)	Grade 0–100, letter grade, per-dimension breakdown, what's missing, verdict
enhance_prompt(prompt, model)	Returns a structured rewrite brief — YOUR LLM writes the enhanced prompt using it
analyze_intent(prompt)	Parse dimensions: camera, motion, lighting, style, mood, gaps
creative_patterns(model)	Community top patterns for this model from the graph brain
community_benchmark(prompt, model)	Compare your prompt against community top scorers
my_story()	Your scoring history, model stats, grade distribution
list_models()	All supported models with medium and core strength

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Supported models

Video

Model	Platforms	Best for	Prompt style
veo3	Higgsfield, Google AI Studio (veo-3.1-generate-preview), Runway	Cinematic brand films, narrative ads, photorealistic motion	Camera move → Subject → Action → Location → Lighting → Mood
seedance	Higgsfield, fal.ai (bytedance/seedance-2.0)	UGC, social-native content, TikTok/Reels performance ads	Natural language, motion-first, authentic feel
kling	Higgsfield (kling3), Kling.ai (kling-v3-text-to-video)	Character animation, product showcases, facial performance	Scene → Characters → Action → Camera → Style; multi-shot labels
runway	Runway (gen4_turbo)	VFX, character performance, cinematic motion	Motion-first — describe what moves, not what exists
wan	fal.ai (fal-ai/wan/v2.7/text-to-video)	4K, 20-second clips, native audio, open-source workflows	Scene → Motion → Sound → Duration → Mood
minimax	fal.ai (fal-ai/minimax/hailuo-02/pro/text-to-video)	Cinematic storytelling, character animation	Natural language + [camera movement] bracket syntax
higgsfield	Higgsfield (native model)	Physics-driven motion — cloth, hair, fluid, particles	Describe materials in motion, not motion abstractly

Sora 2 (OpenAI): API shutdown September 24, 2026. Do not build new dependencies on it — use Runway or Kling instead.

Image

Model	Platforms	Best for	Prompt style
flux	BFL API (flux-pro-v1.1), fal.ai, Replicate	Photorealism, technical photography, product shots	30–80 words; camera body + lens specs; front-load subject
midjourney	Midjourney (v8.1)	Artistic depth, editorial, stylized illustration	Prose + params appended: --ar 16:9 --s 300 --v 8.1 --style raw
ideogram	Ideogram API (V_4), fal.ai	Typography, logos, text-in-image, graphic design	Describe text exactly in quotes inside the prompt
firefly	Adobe Firefly 5 (enterprise)	IP-indemnified commercial assets, 4MP brand content	Natural language + contentClass and style.presets API params

Imagen 4 (Google): deprecated — use gemini-3.5-flash with image output. Dali still scores legacy Imagen prompts via the imagen model key but don't build new things on it.

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Platform supersets

Higgsfield and Runway are aggregator platforms — they proxy multiple underlying models under one API. The model you pick matters more than the platform name:

Platform	Model selector	Underlying model
Higgsfield	veo3	Google Veo 3.1
Higgsfield	seedance	ByteDance Seedance 2.0
Higgsfield	kling3	Kling 3
Higgsfield	wan2-7	Wan 2.7
Higgsfield	image2video	Higgsfield native
Runway	veo3	Google Veo 3.1
Runway	gen4_turbo	Runway Gen 4.5
Runway	seedance	ByteDance Seedance 2.0

Dali scores for the underlying model's native prompt language, not the platform wrapper. Pass the model name (veo3, kling, seedance…), not the platform name.

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Why model-specific?

Generic prompt optimizers don't know that:

• Veo 3.1 needs camera movement specified above everything else
• Kling 3 supports multi-shot scene labels natively in the prompt
• Flux responds to camera body and lens names like a photographer ("Sony A7 IV, 85mm f/1.4")
• Midjourney V8.1 reads prose + parameters, not keyword lists
• Higgsfield simulates physics — you describe materials in motion, not motion abstractly
• Minimax uses [Pan left] bracket syntax for camera moves — plain text camera commands are ignored
• Ideogram V4 needs text quoted exactly in the prompt for typography accuracy
• Wan 2.7 generates native audio — include sound descriptions alongside visuals

Dali has a separate scoring rubric and rewrite brief for each model. Your LLM does the creative rewriting — Dali provides the intelligence.

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MCP resources

creative://guide/veo3       → Veo 3.1 camera language guide
creative://guide/seedance   → Seedance UGC motion guide
creative://guide/kling      → Kling multi-shot + expression guide
creative://guide/runway     → Runway motion-first guide
creative://guide/wan        → Wan 2.7 audio + motion guide
creative://guide/minimax    → Minimax bracket camera guide
creative://guide/higgsfield → Higgsfield physics-motion guide
creative://guide/sora       → Sora 2 guide (API shutdown Sep 24, 2026)
creative://guide/flux       → Flux photography brief guide
creative://guide/midjourney → Midjourney V8.1 + parameters guide
creative://guide/ideogram   → Ideogram V4 typography guide
creative://guide/firefly    → Firefly 5 commercial content guide
creative://guide/imagen     → Imagen 4 guide (deprecated Aug 17, 2026)
creative://models           → All models overview

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Contributing

Model guides live in dali/data/guides/{model}.json on the hosted server. Found practitioner patterns that consistently produce high-grade results? Open an issue with the model, the pattern, and a sample prompt + result. The best contributions come from Reddit, Discord, and YouTube — real practitioners, not official docs.

→ Prompt best practices by model — cheat sheets, do/don't tables, top patterns per model → Dali creative flow skill — install this skill so your LLM follows the score → enhance → generate workflow automatically

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MIT License · Built by Lulu · dali.getlulu.dev