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genpark-multimodal-aspect-ratio-smart-reframe-skill

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Python
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genpark-multimodal-aspect-ratio-smart-reframe-skill

Python 3.9+ License MIT MCP Compatible GenPark AI Zero Dependencies

🌐 GenPark MCP Hub Showcase • 📦 GenPark Official Website • 📖 Documentation

📌 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)

⚡ Key Highlights & Value

  • 🐍 Zero External pip Dependencies: Runs instantaneously on standard Python 3.9+ with zero environment bloat.

  • 🔌 Native Model Context Protocol (MCP): Seamlessly integrates into Claude Desktop, Cursor IDE, CapCut bot automations, and viral video agent pipelines.

  • 🎯 Deterministic & Reliable: 100% predictable input/output contracts with full JSON Schema validation.

  • 🚀 Low Latency: Sub-millisecond execution overhead tailored for high-concurrency batch media processing.

Related MCP server: VE-MCP

🏗️ Architecture & Workflow

graph LR
    User([🎬 Video Creator / Media Swarm]) -->|Audio/Video Telemetry & Transcript| MCP[⚡ MCP Server / Protocol]
    MCP --> Client[🛠️ Media Processing Kernel]
    Client --> Engine[🧠 Pacing & Reframing Pipeline]
    Engine --> Cuts[✂️ Precise Cut-Lists & Telemetry Dossier]
    Cuts --> User

🚀 Quickstart & Usage

1. Direct Python Client Execution

python example_usage.py

2. Programmatic Integration

from client import MultimodalAspectRatioSmartReframeClient

client = MultimodalAspectRatioSmartReframeClient()
result = client.calculate_smart_reframe()
print(result)

🔌 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"]
    }
  }
}

📊 Technical Specifications

Parameter

Type

Required

Description

query_payload

string / dict

Yes

Primary input parameter parsed and executed deterministically

output_format

json / dict

Yes

Standardized response schema containing execution telemetry

❓ 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.

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Maintenance

ActivityMaintained
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Resources

Related MCP Connectors

Related MCP Servers

  • VE-MCP

    assebler-grkha
    A
    license
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    maintenance
    Enables AI agents to autonomously edit videos into publish-ready vertical short-form content, including silence removal, subtitle generation, voiceover synthesis, color grading, and composite pipeline creation.
    MIT

  • vidcut

    mao-data
    A
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    AI-native timeline editor for short-form video. Your agent cuts, captions, adds motion graphics and renders against a real, editable timeline over MCP — not a render queue. Human-in-the-loop by design: it calls request_review and waits while you supervise in a browser. Local-first and AGPL; your footage never leaves your machine.
    1
    AGPL 3.0