Which integrations are available for this server?

Provides multimodal sensory grounding using Meta Ray-Ban audio/vision devices, integrating ambient audio and visual context into the personal AI agent's episodic memory and autonomous action pipeline.

How do I use genpark-meta-muse-multimodal-episodic-resonance-mcp?

1. Click on "Deploy Server". 2. Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state. 3. In the chat, type @ followed by the MCP server name and your instructions, e.g., "@genpark-meta-muse-multimodal-episodic-resonance-mcp What did I see today that made me smile?" That's it! The server will respond to your query, and you can continue using it as needed. Here is a step-by-step guide with screenshots.

genpark-meta-muse-multimodal-episodic-resonance-mcp

🌐 GenPark MCP Hub • 📦 GenPark Official • 📖 Documentation

📌 Overview & Capability

genpark-meta-muse-multimodal-episodic-resonance-mcp is a deterministic, high-performance, zero-dependency Python tool and native Model Context Protocol (MCP) server engineered for next-generation personal AI agents (distilling breakthrough capabilities from Today AI, Manus, Cue, Meta, Muse, and Instinct).

Executive Capability: Multimodal sensory grounding (Meta Ray-Ban audio/vision) integrated with long-term episodic life memory graphs and empathetic emotional resonance (Muse).

⚡ Key Highlights

🐍 Zero External pip Dependencies: Implemented entirely with pure Python standard library for instant zero-overhead execution.
🔌 Native Model Context Protocol (MCP): Plugs directly into any MCP-compliant client via JSON-RPC 2.0 stdio.
🧠 Personal Agent Cognitive Architecture: Fast subconscious intent reflexes, ambient screen/clipboard cues, episodic life memory, and deep autonomous task resolution.
🛡️ Production-Hardened: Comprehensive error handling, boundary validation, and telemetry.

Related MCP server: pasm-mcp-server

🏗️ Architecture

graph LR
    User([👤 User / Ambient Environment]) -->|Sensory Signals & Goals| Core[⚡ genpark-meta-muse-multimodal-episodic-resonance-mcp Engine]
    Core --> Memory[(🧠 Episodic & Context Graph)]
    Core --> Executor[🤖 Autonomous Action Pipeline]
    Executor --> Result[📊 Proactive Action & Telemetry]
    Result --> User

🚀 Quickstart & Usage

1. Direct Python Client Execution

python example_usage.py

2. Programmatic Integration

from client import MetaMuseEpisodicResonance

client = MetaMuseEpisodicResonance()
result = client.run_meta_muse_benchmark()
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-meta-muse-multimodal-episodic-resonance-mcp": {
      "command": "python",
      "args": ["/path/to/genpark-meta-muse-multimodal-episodic-resonance-mcp/mcp_server.py"]
    }
  }
}

Direct MCP Testing

python mcp_server.py --test

📊 Technical Specifications

Parameter	Type	Required	Description
`payload`	`string` / `dict`	Yes	Sensory inputs, task goals, or ambient telemetry
`options`	`dict`	No	Cognitive depth, energy profiles, or execution timeouts

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Maintenance

ActivityMaintained

ResponsivenessNo issues

Resources

GitHub Repository

Need Help?

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