@paradiselabs/mco-protocol
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MCO (Model Configuration Orchestration) MCP Server & Configuration Tool
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# 🚀 MCO Protocol: The Missing Orchestration Layer for MCP
<div align="center">
[](https://www.npmjs.com/package/@paradiselabs/mco-protocol)
[](https://opensource.org/licenses/MIT)
[](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)
</div>
## 🌟 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)
---
<div align="center">
**🌟 Star this repository if MCO helps you build better agents! 🌟**
*Made with ❤️ by [Paradise Labs](https://paradiselabs.co)*
</div>