npm package: @paradiselabs/mco-protocol
Description: MCO (Model Configuration Orchestration) MCP Server & Configuration Tool
Author: ParadiseLabs, LLC
Latest version: 0.2.8

# 🚀 MCO Protocol: The Missing Orchestration Layer for MCP

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[![NPM Version](https://img.shields.io/npm/v/@paradiselabs/mco-protocol.svg)](https://www.npmjs.com/package/@paradiselabs/mco-protocol)
[![License](https://img.shields.io/badge/License-MIT-blue.svg)](https://opensource.org/licenses/MIT)
[![Hackathon](https://img.shields.io/badge/🏆-MCP%20Hackathon%202025-gold)](https://huggingface.co/Agents-MCP-Hackathon)

**Completing the Agentic Trifecta: MCP + A2P + MCO**

*Transform unreliable agents into structured, autonomous workflows with progressive revelation and persistent memory.*

[🎮 **Live Demo**](https://huggingface.co/spaces/paradiselabs/mco-protocol-demo) • [📦 **NPM Package**](https://www.npmjs.com/package/@paradiselabs/mco-protocol) • [📖 **Documentation**](https://github.com/paradiselabs-ai/MCO-Protocol/blob/main/docs)

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## 🌟 The Agentic Trifecta

```mermaid
graph TB
    subgraph "The Foundation of Autonomous AI"
        MCP[📊 MCP<br/>Model Context Protocol<br/><i>Data Integration</i>]
        A2P[🤝 A2P<br/>Agent-to-Agent Protocol<br/><i>Communication</i>]
        MCO[🎛️ MCO<br/>Model Configuration Orchestration<br/><i>Reliable Orchestration</i>]
    end
    
    MCP --> AGENT[🤖 Autonomous Agent]
    A2P --> AGENT
    MCO --> AGENT
    
    AGENT --> RESULT[✨ Production-Ready<br/>Autonomous AI]
    
    style MCO fill:#667eea,stroke:#333,stroke-width:3px,color:#fff
    style RESULT fill:#2ecc71,stroke:#333,stroke-width:2px,color:#fff
```

**Why MCO is Essential:**
- 📊 **MCP** connects agents to data sources → *"What can I access?"*
- 🤝 **A2P** enables agent communication → *"How do we coordinate?"*  
- 🎛️ **MCO** ensures reliable execution → *"How do we actually get things done?"*

## 🎯 The Problem MCO Solves

Traditional autonomous agents (AutoGPT, BabyAGI) suffer from:
- 🔄 **Endless loops** and failed executions
- 🧠 **Context overload** leading to poor decisions  
- 🎯 **Lack of focus** on core objectives
- 📉 **Unpredictable reliability** in production

## 💡 The MCO Solution: Progressive Revelation

```mermaid
graph LR
    subgraph "Traditional Approach"
        T1[Agent] --> T2[Everything at Once<br/>📚 Core + Features + Styles + Context]
        T2 --> T3[❌ Overwhelmed<br/>Loops & Failures]
    end
    
    subgraph "MCO Progressive Revelation"
        M1[Agent] --> M2[🧠 Persistent Memory<br/>Core + Success Criteria]
        M2 --> M3[⚡ Step 1: Focus on Core]
        M3 --> M4[✨ Step 2: + Features Injection]
        M4 --> M5[🎨 Step 3: + Styles Injection]
        M5 --> M6[✅ Reliable Completion]
    end
    
    style T3 fill:#e74c3c,color:#fff
    style M6 fill:#2ecc71,color:#fff
```

## 🛠️ How MCO Works

### SNLP (Syntactic Natural Language Programming)

MCO uses a revolutionary programming language that combines structured syntax with natural language:

```yaml
# mco.core - Always in persistent memory
@workflow "Research Assistant"
>NLP An AI assistant that conducts autonomous research with reliability.

@data:
  topic: "AI Agent Orchestration"
  findings: []

@agents:
  researcher:
    steps:
      - "Research the topic thoroughly"
      - "Analyze patterns and insights"  
      - "Create comprehensive report"

# mco.features - Injected at 33% progress
@feature "Data Visualization"
>NLP Create charts and graphs when appropriate to enhance understanding.

# mco.styles - Injected at 66% progress  
@style "Professional Formatting"
>NLP Use clear headings, bullet points, and executive summary format.
```

### Orchestration Flow

```mermaid
sequenceDiagram
    participant AF as Agent Framework
    participant MCO as MCO MCP Server
    participant SNLP as SNLP Files
    
    Note over AF,SNLP: Progressive Revelation in Action
    
    AF->>MCO: start_orchestration()
    MCO->>SNLP: Load mco.core + mco.sc
    MCO-->>AF: orchestration_id
    
    AF->>MCO: get_next_directive()
    Note right of MCO: Persistent Memory Only
    MCO-->>AF: Step 1 + Core Context
    
    AF->>MCO: complete_step(result)
    MCO->>MCO: Evaluate against success criteria
    
    AF->>MCO: get_next_directive()
    Note right of MCO: Strategic Injection
    MCO->>SNLP: Inject mco.features
    MCO-->>AF: Step 2 + Core + Features
    
    AF->>MCO: complete_step(result)
    
    AF->>MCO: get_next_directive()
    MCO->>SNLP: Inject mco.styles  
    MCO-->>AF: Step 3 + Core + Features + Styles
    
    AF->>MCO: complete_step(result)
    MCO-->>AF: ✅ Workflow Complete
```

## 🚀 Quick Start

### Installation

```bash
npm install -g @paradiselabs/mco-protocol
```

### Create Your First Workflow

```bash
# Initialize new MCO project
mco init my-research-assistant

# Opens configuration tool in browser
# Generates: mco.core, mco.sc, mco.features, mco.styles
```

### Add to Any MCP-Enabled Framework

```json
{
  "mcpServers": {
    "mco-orchestration": {
      "command": "npx",
      "args": ["@paradiselabs/mco-protocol", "--config-dir", "./my-research-assistant"]
    }
  }
}
```

### Use in Your Agent Framework

```python
# Works with ANY MCP-enabled framework
directive = mcp_client.call_tool("get_next_directive")
result = execute_task(directive.instruction)
mcp_client.call_tool("complete_step", step_id=directive.step_id, result=result)
```

## 🎭 Live Demo

**🎮 [Try the Interactive Demo](https://huggingface.co/spaces/paradiselabs/mco-protocol-demo)**

Generate real SNLP configurations and see MCO in action with live MCP server simulation.

## 📊 Architecture Overview

```mermaid
graph TB
    subgraph "MCO MCP Server"
        CLI[🖥️ CLI Interface<br/>mco init, serve, validate]
        CONFIG[🎛️ Configuration Tool<br/>Web-based SNLP Generator]
        PARSER[📝 SNLP Parser<br/>@markers + >NLP sections]
        ENGINE[⚡ Orchestration Engine<br/>Progressive Revelation]
        MCP[📡 MCP Tool Provider<br/>start_orchestration, get_next_directive]
    end
    
    subgraph "SNLP Files"
        CORE[🧠 mco.core<br/>Persistent Memory]
        SC[🎯 mco.sc<br/>Success Criteria]  
        FEATURES[✨ mco.features<br/>Strategic Injection]
        STYLES[🎨 mco.styles<br/>Strategic Injection]
    end
    
    subgraph "Agent Frameworks"
        AUTOGPT[🤖 AutoGPT]
        CREWAI[👥 CrewAI]
        LANGGRAPH[🕸️ LangGraph]
        CUSTOM[⚙️ Custom Agents]
    end
    
    CLI --> CONFIG
    CONFIG --> CORE & SC & FEATURES & STYLES
    PARSER --> CORE & SC & FEATURES & STYLES
    PARSER --> ENGINE
    ENGINE --> MCP
    
    MCP <==> AUTOGPT
    MCP <==> CREWAI  
    MCP <==> LANGGRAPH
    MCP <==> CUSTOM
    
    style MCO fill:#667eea,color:#fff
    style CORE fill:#e8f5e9
    style SC fill:#e3f2fd
    style FEATURES fill:#fff3e0
    style STYLES fill:#fce4ec
```

## 🏆 Perfect for MCP Hackathon 2025

**Track 1: MCP Server Implementation** ✅

MCO exemplifies the future of MCP by:
- 🔧 **Extending MCP's Vision**: Making agent orchestration as standardized as data access
- 🎯 **Solving Real Problems**: Transforming unreliable agents into production-ready systems
- 🚀 **Ready for Production**: Live NPM package, working implementation
- 🌟 **Innovative Approach**: First orchestration protocol designed specifically for MCP ecosystem

## 📈 Before vs After

```mermaid
graph LR
    subgraph "Before MCO"
        B1[🤖 Agent] --> B2[❓ Vague Prompts]
        B2 --> B3[🔄 Loops & Failures]
        B3 --> B4[😤 Manual Intervention]
    end
    
    subgraph "After MCO"
        A1[🤖 Agent] --> A2[🎛️ MCO Orchestration]
        A2 --> A3[📋 Structured Steps]
        A3 --> A4[✅ Reliable Completion]
    end
    
    style B3 fill:#e74c3c,color:#fff
    style A4 fill:#2ecc71,color:#fff
```

## 🔗 Available MCP Tools

MCO exposes these tools through the MCP protocol:

| Tool | Description | Use Case |
|------|-------------|----------|
| `start_orchestration` | Initialize new workflow | Begin autonomous task |
| `get_next_directive` | Get next step with context | Progressive execution |
| `complete_step` | Mark step complete | Track progress |
| `get_workflow_status` | Check progress | Monitoring |
| `evaluate_against_criteria` | Quality assessment | Success validation |

## 🎨 CLI Commands

```bash
mco init [project-name]     # Create new MCO project
mco validate [config-dir]   # Validate SNLP files  
mco serve [config-dir]      # Start MCP server
mco templates              # List available templates
```

## 🤝 Contributing

We welcome contributions! MCO is designed to become the standard for agent orchestration.

1. Fork the repository
2. Create your feature branch (`git checkout -b feature/amazing-feature`)
3. Commit your changes (`git commit -m 'Add amazing feature'`)
4. Push to the branch (`git push origin feature/amazing-feature`)
5. Open a Pull Request

## 📜 License

MIT License - see [LICENSE](LICENSE) file for details.

## 🚀 Join the Revolution

**MCO Protocol is live and ready to transform how you build autonomous agents.**

- 📦 **Install**: `npm install -g @paradiselabs/mco-protocol`
- 🎮 **Demo**: [Interactive Gradio Space](https://huggingface.co/spaces/paradiselabs/mco-protocol-demo)
- 💬 **Discord**: [Join our community](https://discord.gg/uQ69vc4Agc)
- 🐦 **Twitter**: [@paradiselabs_ai](https://twitter.com/paradiselabs_ai)

---

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**🌟 Star this repository if MCO helps you build better agents! 🌟**

*Made with ❤️ by [Paradise Labs](https://paradiselabs.co)*

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