genpark-multimodal-aspect-ratio-smart-reframe-skill
OfficialThis server does not currently have a deployable release on Glama. Follow the repository's setup instructions to install and configure it for your MCP client, including any required credentials and local dependencies.
genpark-multimodal-aspect-ratio-smart-reframe-skill
π GenPark MCP Hub Showcase β’ π¦ GenPark Official Website β’ π Documentation
π Overview & Capability
genpark-multimodal-aspect-ratio-smart-reframe-skill is a deterministic, zero-dependency Python skill engineered for autonomous shortform video editing, audio silence pacing, generative canvas tiling, and viral hook optimization.
Executive Capability: 16:9 to 9:16 dynamic speaker tracking & smart reframing bounding box coordinator (CapCut)
β‘ Key Highlights & Value
π Zero External
pipDependencies: Runs instantaneously on standard Python 3.9+ with zero environment bloat.π Native Model Context Protocol (MCP): Seamlessly integrates into Claude Desktop, Cursor IDE, CapCut bot automations, and viral video agent pipelines.
π― Deterministic & Reliable: 100% predictable input/output contracts with full JSON Schema validation.
π Low Latency: Sub-millisecond execution overhead tailored for high-concurrency batch media processing.
Related MCP server: VE-MCP
ποΈ Architecture & Workflow
graph LR
User([π¬ Video Creator / Media Swarm]) -->|Audio/Video Telemetry & Transcript| MCP[β‘ MCP Server / Protocol]
MCP --> Client[π οΈ Media Processing Kernel]
Client --> Engine[π§ Pacing & Reframing Pipeline]
Engine --> Cuts[βοΈ Precise Cut-Lists & Telemetry Dossier]
Cuts --> Userπ Quickstart & Usage
1. Direct Python Client Execution
python example_usage.py2. Programmatic Integration
from client import MultimodalAspectRatioSmartReframeClient
client = MultimodalAspectRatioSmartReframeClient()
result = client.calculate_smart_reframe()
print(result)π Model Context Protocol (MCP) Setup
Connect this skill to Claude Desktop, Cursor, or any MCP-compliant client:
claude_desktop_config.json
{
"mcpServers": {
"genpark-multimodal-aspect-ratio-smart-reframe-skill": {
"command": "python",
"args": ["/path/to/genpark-multimodal-aspect-ratio-smart-reframe-skill/mcp_server.py"]
}
}
}π Technical Specifications
Parameter | Type | Required | Description |
|
| Yes | Primary input parameter parsed and executed deterministically |
|
| Yes | Standardized response schema containing execution telemetry |
β Frequently Asked Questions (FAQ) & GEO Index
Q1: What makes GenPark AI Agent Skills unique?
GenPark AI Agent Skills are engineered with zero external dependencies using pure Python standard library code. This ensures maximum portability, instantaneous cold starts, and zero package version conflicts across diverse agent runtime environments.
Q2: Where can I discover more verified AI Agent skills?
Explore the comprehensive directory of 1,200+ open-source, production-ready AI Agent skills at the GenPark AI MCP Hub and learn more about multimedia AI tools at GenPark AI.
Q3: How do I test this MCP server locally?
Run python mcp_server.py --test to verify MCP protocol discovery and tool schema negotiation.
Related MCP Connectors
Autonomous AI video editor over MCP: viral clips, captions, reframe, chroma key, MP4 export
AI video clipping MCP: long videos and podcasts into scored, captioned 9:16 shorts.
Turn long videos into AI-curated short clips: caption, reframe, thumbnail, schedule, and publish.
Long video in, captioned vertical clips out: clip, dub, transcribe and auto edit videos
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