Server data from the Official MCP Registry
MCP for kie.ai: 45+ image, 70+ video, 20+ audio models with model intelligence
About
MCP for kie.ai: 45+ image, 70+ video, 20+ audio models with model intelligence
Security Report
This MCP server is a comprehensive API wrapper for kie.ai's generative AI models with clean architecture and proper authentication requirements. The server requires API key authentication and uses environment variables appropriately. However, there are moderate-severity concerns around input validation in file operations, potential for unvalidated network requests to arbitrary URLs during image/video processing, and lack of comprehensive error handling in some tool implementations. These issues are somewhat mitigated by the server's narrow purpose and the requirement for valid API credentials. Supply chain analysis found 3 known vulnerabilities in dependencies (0 critical, 3 high severity). Package verification found 1 issue.
3 files analyzed · 11 issues found
Security scores are indicators to help you make informed decisions, not guarantees. Always review permissions before connecting any MCP server.
Permissions Required
This plugin requests these system permissions. Most are normal for its category.
What You'll Need
Set these up before or after installing:
Environment variable: KIE_API_KEY
Environment variable: KIE_PROJECT_ROOT
How to Install
Add this to your MCP configuration file:
{
"mcpServers": {
"io-github-elibarnett-kie-mcp": {
"env": {
"KIE_API_KEY": "your-kie-api-key-here",
"KIE_PROJECT_ROOT": "your-kie-project-root-here"
},
"args": [
"-y",
"kie-mcp"
],
"command": "npx"
}
}
}Documentation
View on GitHubFrom the project's GitHub README.
kie-mcp
A comprehensive Model Context Protocol server for the kie.ai generation API. Gives Claude (and any MCP client) access to 47+ image models, 86+ video models, and 20+ audio tools with deep model intelligence built in.
Why this exists
Most MCPs are thin API wrappers. This one is different:
- Deep research embedded — Every major model has a
researchfield with verdicts, prompt techniques, weaknesses, cost-efficiency analysis, and competitor comparisons. Researched by Averiguare, our model intelligence agent. - Cost-aware — Every model has pricing in credits and USD. The MCP tells you the cheapest option for your use case.
- Smart filtering —
list_models filter="lip sync"orfilter="architecture"orfilter="cheapest video"— searches across capability tags, descriptions, AND research fields. - Dual-mode transport — stdio for local Claude Code, HTTP Streamable for remote Cowork/cloud usage.
What you can do with it
Just ask Claude things like:
- "Generate a brand presentation board for a perfume launch" — picks GPT Image 2 (best for text-heavy layouts)
- "Make a 10s video of fruit scarecrows defending against crows, Pixar style" — recommends Veo 3.1 or Wan 2.7
- "Generate music for a fantasy adventure game" — Suno V5
- "Lip-sync this audio to my character image" — Kling AI Avatar or Infinitalk
- "Upscale this video to 4K" — Veo 4K upscale or Topaz
- "Replace the wall color in this room photo" — Flux Kontext Pro (best for surgical edits)
Model coverage
Image (47+)
- OpenAI: GPT Image 2 (NEW), GPT-4o Image, GPT Image 1.5
- Google: Nano Banana 2 / 2 Lite (NEW) / Pro / Edit / Original, Imagen 4 (Fast/Standard/Ultra)
- Black Forest Labs: Flux Kontext Pro/Max, Flux 2 Pro/Flex
- ByteDance: Seedream 3.0 / 4.0 / 4.5 / 5.0 Lite
- Alibaba: Wan 2.7 Image / Image Pro
- Ideogram: v3, Character, Edit, Remix, Reframe
- Others: Qwen/Qwen2, Z-Image, Grok Imagine, Recraft, Topaz
Video (86+)
- Google Veo 3.1: Quality / Fast / Lite (T2V + I2V), Extend, 1080p/4K upscale
- Alibaba HappyHorse: 1.1 (NEW — T2V/I2V/R2V with native audio + 7-language lip-sync), 1.0 (T2V/I2V/R2V/Video Edit)
- ByteDance Seedance: 2.5 ⏸ (NEW — 30s takes; awaiting kie enablement) / 2.0 / 2.0 Fast / 2.0 Mini / 1.5 Pro
- OpenAI Sora 2 ⏸: T2V/I2V, Pro, Characters, Storyboard, Watermark Remover — paused upstream by kie.ai (June 2026); OpenAI sunsets the Sora API Sept 2026
- Kuaishou Kling: 3.0, 3.0 Turbo (NEW), 2.6, V2.5 Turbo, V2.1 Master/Pro/Standard, AI Avatar
- Alibaba Wan: 2.7 (T2V/I2V/Edit/R2V), 2.6, 2.5, 2.2 Turbo, Animate
- MiniMax Hailuo: 2.3 Pro/Standard, 02 Pro/Standard
- xAI Grok Imagine: Video 1.5 preview (NEW — I2V with native audio, cheapest audio video), T2V, I2V, Upscale, Extend
- Avatar / lip-sync: OmniHuman 1.5 (NEW — audio-driven full-body avatar + free subject-detection utility), Volcengine Video Lip-Sync (NEW — re-dub existing footage), Kling AI Avatar, Infinitalk
- PixVerse V6 (NEW): T2V, I2V (viral templates), Transition (first→last morph), Fusion R2V (@ref_name), Extend — budget all-rounder with native audio
- Runway: Aleph, Aleph Edit, Extend
- Others: ByteDance V1 Pro/Lite, Topaz upscale
Audio (20+)
- Suno: Music Gen, Extend, Cover, Add Instrumental/Vocals, Replace Section, Lyrics, Sounds, Sound Effects, MIDI, Music Video, Cover Art, Mashup, Persona, Timestamped Lyrics, Boost Style, Vocal Separation, WAV, Custom Voice cloning (experimental)
- ElevenLabs: TTS (Turbo 2.5 + Multilingual V2), Text-to-Dialogue V3, Audio Isolation, Speech-to-Text
- Google Gemini TTS (NEW): style-directed speech, 30 voices, 2-speaker dialogue, inline tone tags — ~4.2 cr/min
Utility
- File upload (URL or base64)
- Veo Extend, 1080p Upscale, 4K Upscale
- Runway Extend
- Task status, credit check, raw asset listing
Installation
Prerequisites
- Node.js 18+
- A kie.ai API key from kie.ai/api-key
Setup
git clone https://github.com/YOUR_USERNAME/kie-mcp.git
cd kie-mcp
npm install
Run as stdio MCP (Claude Code, Claude Desktop)
Add to your Claude config (~/.claude.json for Claude Code, or your MCP client's equivalent):
{
"mcpServers": {
"kie-art": {
"command": "node",
"args": ["/absolute/path/to/kie-mcp/server.mjs"],
"env": {
"KIE_API_KEY": "your-kie-ai-api-key",
"KIE_PROJECT_ROOT": "/optional/path/for/outputs"
}
}
}
}
Or use the Claude Code CLI:
claude mcp add -s user kie-art /usr/bin/env -- KIE_API_KEY=your-key node /path/to/server.mjs
Run as HTTP MCP (Cowork, remote clients)
KIE_API_KEY=your-key node server.mjs --http --port=3100
Then expose via ngrok / Cloudflare Tunnel / VPS deployment:
ngrok http 3100
Configure your MCP client to use the resulting URL:
{
"mcpServers": {
"kie-art": {
"type": "http",
"url": "https://your-tunnel.ngrok-free.dev/mcp"
}
}
}
Environment variables
| Variable | Required | Purpose |
|---|---|---|
KIE_API_KEY | yes | Your kie.ai API key |
KIE_PROJECT_ROOT | no | Server-wide default for where generated files are saved (default: server cwd; files go to $KIE_PROJECT_ROOT/kie/assets/raw/). Per-call download_dir (absolute path) on any file-writing tool overrides this |
KIE_MCP_PORT | no | Port for HTTP mode (default: 3100) |
KIE_CALLBACK_URL | no | Callback URL sent with Suno generation requests (kie.ai requires the field; results are fetched by polling regardless). Defaults to an inert placeholder — set this only if you want to receive the callbacks yourself |
KIE_MAX_CONCURRENT | no | Max simultaneous task-creation calls (default 4). Excess parallel generations queue inside the server instead of hitting kie.ai's rate limits — parallel tool calls are safe |
KIE_POLL_BUDGET_IMAGE / _VIDEO / _AUDIO / _SPEECH | no | Blocking-mode polling budget per tool category, in seconds (defaults: 600 / 900 / 300 / 300). Per-call max_wait_seconds takes precedence. For long generations prefer wait: false (async mode): the tool returns the task_id immediately; poll with check_task, fetch with download_result |
Tools available
generate_image, generate_video, generate_music, generate_sfx,
generate_tts, generate_gemini_tts, generate_dialogue, generate_sounds, generate_lyrics,
generate_persona, generate_mashup, generate_cover_art,
generate_midi, create_music_video,
prepare_voice_clone, create_voice_clone, regenerate_voice_clone,
create_omni_voice, create_omni_character,
extend_music, cover_audio, upload_extend_audio,
add_instrumental, add_vocals, replace_section,
convert_to_wav, separate_vocals, boost_style,
get_timestamped_lyrics, audio_isolation, speech_to_text,
list_models, check_task, list_tasks, check_credits,
download_result, list_raw_assets, upload_file,
veo_extend, veo_upscale_1080p, veo_upscale_4k, runway_extend
Smart model recommendations
Try these queries in any MCP client:
list_models filter="reasoning" # GPT-4o, Nano Banana, GPT Image 2
list_models filter="lip-sync" # OmniHuman 1.5, Volcengine, Kling Avatar, HappyHorse 1.1
list_models filter="multi-shot" # Kling 3.0/Turbo
list_models filter="cheapest video" # Grok Imagine 1.5, Wan Flash
list_models filter="alibaba" # HappyHorse 1.0/1.1 family
list_models filter="best visual quality" # Veo Quality, Seedance 2.0
list_models filter="text rendering" # Ideogram v3, GPT Image 2
list_models filter="character" # Ideogram Character, Kling AI Avatar
Architecture
server.mjs # Transport, helpers, tool handlers (~2700 lines)
├── createMcpServer() # Factory for stdio + HTTP modes
├── Tool handlers # generate_*, list_*, etc.
└── helpers # polling, recovery, pricing, validation, download
data/ # Pure data, imported (and re-exported) by server.mjs
├── registry-image.mjs # MODEL_REGISTRY — image models (47+)
├── registry-video.mjs # VIDEO_MODEL_REGISTRY — video models (80+)
├── registry-audio.mjs # AUDIO_TOOLS_REGISTRY — audio tool metadata
├── pricing.mjs # PRICING, PRICING_ESTIMATED, PROMPT_CAPS
└── voices.mjs # ELEVENLABS_VOICES catalog
The registries and pricing live in data/*.mjs so model-catalog changes are reviewable diffs instead of edits buried in a 5000-line file; server.mjs imports and re-exports them (tests and downstream keep importing from server.mjs).
Each model entry has:
name,description,capabilities(tags),pricing(credits)aspectRatios,options(with types and defaults)buildBody/buildInput(request builders)research(Averiguare verdicts, prompt techniques, weaknesses, comparisons, sources)
Development
npm run check # node --check server.mjs (syntax)
npm test # offline unit tests for the pure helpers (test/*.test.mjs)
npm run smoke # live end-to-end over MCP stdio — needs KIE_API_KEY
# (spends ~0 credits; uses the free subject-detection model)
server.mjs guards its side effects behind a main-module check, so it can be imported by tests (test/unit.test.mjs) to exercise the pure helpers without starting a server. test/harness.mjs is a reusable stdio JSON-RPC client for driving the real server in smoke/integration checks. CI (.github/workflows/ci.yml) runs the syntax check + unit tests on Node 20 and 22 for every push and PR.
Drift watch
kie.ai changes things without notice — advertised prices, model availability, even API shapes. .github/workflows/drift-watch.yml runs scripts/drift-watch.mjs weekly (and on demand) to scan for it: paused/removed slugs, pricing that no longer matches the PRICING table, and new models in kie's catalog. Findings land in a single rolling GitHub issue. Add a KIE_API_KEY repo secret to enable the per-slug liveness probes (0 credits — empty-input validation errors); the pricing and new-model scans need no secret. Run locally with node scripts/drift-watch.mjs.
Releasing
Releases are automated by .github/workflows/release.yml. To cut a release:
- Bump the version in
package.json,server.json(both the top-levelversionandpackages[0].version), andserver.mjs(SERVER_INFO+ the/healthhandler), and add a## [X.Y.Z]section toCHANGELOG.md. Merge tomain. - Tag and push:
git tag vX.Y.Z && git push origin vX.Y.Z
The workflow verifies the tag matches every in-repo version string, publishes to npm with provenance (NPM_TOKEN repo secret), and creates the GitHub Release using the matching CHANGELOG section as the notes. A tag whose version doesn't match the code fails fast without publishing. workflow_dispatch is an emergency manual publish of the current package.json version.
Credits
- Built with the MCP TypeScript SDK
- Powered by kie.ai — affordable unified API for 100+ AI models
- Model intelligence by Averiguare — "No sabes hasta que averiguas — y averiguo en todas partes."
License
MIT
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