dali-mcp
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Bu listing icin henuz AI raporu yok.
Creative intelligence MCP — score your prompt before you spend the credit
Dali by Lulu
dali.getlulu.dev · Install · Live stats · Lulu
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. Every wasted generation has a real cost (a Seedance retry is ~$6) — the live dashboard tracks what the community has saved by catching bad prompts before they burned a credit.
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.
Contents
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
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 |
score_and_enhance(prompt, generator) |
Score + enhance in one round-trip — returns original score, enhanced prompt, and new score |
track_enhancement(original, enhanced, generator) |
Record a before/after pair in the graph brain — trains community patterns |
suggest_generator(concept, budget_usd_max) |
Pick the best model for your concept + budget constraint |
score_variations(prompts, generator) |
Rank a list of prompt variants in one call — returns them highest to lowest score |
dali_version() |
Server version + changelog |
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 |
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-flashwith image output. Dali still scores legacy Imagen prompts via theimagenmodel key but don't build new things on it.
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.
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.
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
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
MIT License · Built by Lulu · dali.getlulu.dev
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