tmancino/arta-tactical-mcp-server

ARTA Tactical MCP Server

Adaptive Rules and Tactical Analysis (ARTA) - A sophisticated Model Context Protocol (MCP) server that serves as a tactical orchestration platform for intelligent decision-making across complex domains.

🎯 Overview

ARTA is a tactical orchestration platform that transforms complex decision-making through intelligent rule-based analysis, multi-agent coordination, and real-time data integration. ARTA's framework applies across domains requiring sophisticated situational analysis and strategic planning.

🙏 Built on ARTA Foundation

This tactical MCP server builds upon the excellent foundational work of the ARTA project by MAIF:

• Original ARTA Project: https://maif.github.io/arta/
• ARTA Core Engine: Provides the rules engine and evaluation framework
• MAIF Team: Created the innovative adaptive rules architecture

Our implementation extends ARTA's capabilities with MCP protocol integration, agent communication, and tactical analysis patterns while maintaining compatibility with the core ARTA rules format.

Core Philosophy

ARTA operates on the principle that effective tactical decision-making requires:

• 🧠 Intelligent Analysis: ML-powered pattern recognition and prediction
• 🤝 Multi-Agent Coordination: Orchestrating specialized AI agents and services
• 📊 Evidence-Based Reasoning: Database integration and historical analysis
• ⚡ Real-Time Adaptation: Dynamic response to changing conditions
• 🎯 Strategic Focus: Entity identification and influence mapping

🏗️ System Architecture

graph TB
    subgraph "ARTA Tactical MCP Server"
        A[Rules Engine] --> B[ML Pipeline Integration]
        A --> C[Database Connectors]
        A --> D[Agent Orchestrator]
        A --> E[Prediction Calculator]
        
        B --> F[Content Analysis]
        B --> G[Entity Classification]
        B --> H[Network Analysis]
        
        D --> I[Specialist Agents]
        D --> J[External Services]
        D --> K[MCP Agents]
        
        E --> L[Ensemble Models]
        E --> M[Uncertainty Quantification]
    end
    
    subgraph "Agent Ecosystem"
        I --> N[Intelligence Agent]
        I --> O[Threat Assessment Agent]
        I --> P[Strategy Agent]
        I --> Q[Domain Expert Agents]
    end
    
    subgraph "Data Sources"
        C --> R[Historical Database]
        C --> S[Real-time Monitoring]
        C --> T[External APIs]
    end
    
    subgraph "Client Applications"
        U[LLM Clients] --> A
        V[Strategic Platforms] --> A
        W[Decision Support Systems] --> A
    end

🚀 Key Capabilities

1. Tactical Rules Engine

• YAML-Based Configuration: Human-readable rule definitions
• Conditional Logic: Complex condition evaluation with operators
• Action Orchestration: Database queries, ML models, agent coordination
• Dynamic Weighting: Context-adaptive rule prioritization

2. Multi-Agent Orchestration

# Example: Coordinating specialist agents for comprehensive analysis
multi_agent_coordination:
  - name: "strategic_situation_assessment"
    agents:
      - intelligence_gathering_agent
      - threat_assessment_agent  
      - opportunity_analysis_agent
      - strategic_planning_agent
    execution: "parallel_with_synthesis"
    confidence_threshold: 0.8

3. ML Pipeline Integration

• Named Entity Recognition: GLiNeR models for tactical entity identification
• Sentiment Analysis: Content mood and complexity assessment
• Network Analysis: Relationship mapping and influence scoring
• Ensemble Predictions: Multiple model consensus with uncertainty quantification

4. Database Intelligence

# Example: Historical performance analysis
database_analysis:
  query: |
    SELECT entity_id, success_rate, avg_confidence 
    FROM performance_metrics 
    WHERE action_date >= DATE_SUB(NOW(), INTERVAL 2 YEAR)
  result_processing: "statistical_analysis"
  confidence_weighting: true

5. Real-Time Decision Support

• Event Detection: Monitoring for critical situation changes
• Rapid Response: Sub-minute tactical assessment pipelines
• Alert Coordination: Multi-channel notification systems
• Human-in-the-Loop: Escalation for high-stakes decisions

🔧 MCP Tools Exposed

The ARTA MCP server exposes the following tools for client applications:

Core Analysis Tools

Agent Coordination Tools

Intelligence Tools

📋 Use Cases

🛡️ Defense & Security

• Threat Assessment: Multi-source intelligence fusion and risk analysis
• Mission Planning: Resource allocation and timeline optimization
• Intelligence Coordination: Multi-agency information sharing and synthesis
• Operational Support: Real-time decision support for tactical operations

🏢 Corporate Strategy

• Market Intelligence: Competitive analysis and opportunity identification
• Stakeholder Management: Influence mapping and relationship analysis
• Risk Assessment: Multi-factor risk evaluation and mitigation planning
• Strategic Planning: Long-term tactical coordination and execution

🌐 Crisis Management

• Situation Assessment: Real-time analysis of evolving situations
• Resource Coordination: Multi-agency response orchestration
• Public Communications: Message coordination and stakeholder engagement
• Recovery Planning: Post-crisis analysis and improvement recommendations

🚀 Getting Started

Prerequisites

• Node.js 18.0.0 or higher
• TypeScript 5.0+
• Access to required databases and ML models
• MCP-compatible client (Claude Desktop, etc.)

Installation

# Clone the repository
git clone https://github.com/your-org/arta-tactical-mcp-server.git
cd arta-tactical-mcp-server

# Install Node.js dependencies
npm install

# Install Python dependencies for ARTA engine
cd python
pip install -r requirements.txt
cd ..

# Build the TypeScript server
npm run build

# Start the server
npm start

Development Setup

# For development with auto-reload
npm run dev

# Type checking
npm run type-check

# Build only
npm run build

Configuration

{
  "mcp": {
    "servers": {
      "arta": {
        "command": "node",
        "args": ["./dist/server.js"],
        "env": {
          "ARTA_DB_URL": "postgresql://user:pass@host:5432/arta_db",
          "ARTA_RULES_PATH": "./rules",
          "ARTA_ML_MODELS_PATH": "./models",
          "ARTA_AGENT_REGISTRY_URL": "https://agents.your-org.com"
        }
      }
    }
  }
}

🔨 Example Usage

Basic Rule Evaluation

// Client code example
const result = await mcp.callTool("arta_evaluate_rules", {
  query: "Assess the viability of the proposed defense authorization bill",
  intent: "outcome_prediction",
  entities: [
    { type: "BILL", text: "Defense Authorization Act 2025" },
    { type: "COMMITTEE", text: "Armed Services Committee" }
  ],
  context: {
    session_phase: "early_session",
    political_climate: "unified_government"
  }
});

console.log(`Prediction: ${result.probability}`);
console.log(`Confidence: ${result.confidence}`);
console.log(`Explanation: ${result.explanation}`);

Multi-Agent Coordination

// Coordinate specialist agents for comprehensive analysis
const analysis = await mcp.callTool("arta_coordinate_agents", {
  agent_team: [
    "intelligence_gathering_agent",
    "threat_assessment_agent", 
    "strategic_planning_agent"
  ],
  coordination_strategy: "parallel_execution_with_synthesis",
  timeout: 120
});

console.log(`Analysis Complete: ${analysis.synthesis.summary}`);
console.log(`Tactical Recommendations: ${analysis.recommendations}`);

Real-Time Intelligence

// Gather comprehensive intelligence on a target entity
const intelligence = await mcp.callTool("arta_gather_intelligence", {
  target_entity: "Strategic Infrastructure Committee",
  intelligence_types: ["composition", "influence_network", "decision_patterns"],
  sources: ["database", "ml_analysis", "real_time_monitoring"]
});

console.log(`Intelligence Report: ${intelligence.executive_summary}`);
console.log(`Key Insights: ${intelligence.tactical_insights}`);

🤝 Agent Integration Examples

LangChain Agent Integration

langchain_coordination:
  - name: "expert_consultation_workflow"
    agents:
      - name: "legal_analysis_agent"
        framework: "langchain"
        model: "gpt-4"
        specialization: "constitutional_law"
      - name: "financial_impact_agent"
        framework: "langchain" 
        model: "claude-3"
        specialization: "budget_analysis"
    synthesis: "expert_weighted_consensus"

Custom MCP Agent Coordination

mcp_agent_coordination:
  - name: "distributed_analysis_network"
    agents:
      - name: "intelligence_mcp_server"
        type: "mcp"
        endpoint: "mcp://intelligence.tactical.com:8080"
        capabilities: ["data_gathering", "pattern_recognition"]
      - name: "strategy_mcp_server"
        type: "mcp"
        endpoint: "mcp://strategy.tactical.com:8080"
        capabilities: ["planning", "optimization"]
    coordination: "federated_consensus"

External API Agent Integration

api_agent_integration:
  - name: "external_expert_network"
    agents:
      - name: "threat_intelligence_api"
        type: "rest_api"
        endpoint: "https://threat-intel.api.com/v1/analyze"
        authentication: "api_key"
        specialization: "threat_assessment"
      - name: "geospatial_analysis_api"
        type: "rest_api"
        endpoint: "https://geo-intel.api.com/v2/analyze"
        authentication: "oauth2"
        specialization: "location_intelligence"

📊 Performance & Monitoring

System Metrics

• Rule Evaluation Time: < 100ms for simple rules, < 2s for complex ML pipelines
• Agent Coordination: Parallel execution with sub-5s synthesis
• Database Queries: Optimized with connection pooling and caching
• ML Model Inference: GPU-accelerated with batch processing

Quality Assurance

• Prediction Accuracy Monitoring: Continuous validation against outcomes
• Agent Performance Tracking: Response time and accuracy metrics
• Drift Detection: ML model performance degradation alerts
• Human Oversight Integration: Escalation for high-stakes decisions

🔐 Security & Privacy

Data Protection

• Encryption at Rest: AES-256 for sensitive tactical data
• Secure Communications: TLS 1.3 for all agent communications
• Access Controls: Role-based permissions with audit logging
• Data Minimization: Configurable data retention policies

Agent Security

• Agent Authentication: PKI-based agent identity verification
• Secure Coordination: Encrypted agent-to-agent communications
• Sandboxed Execution: Isolated agent runtime environments
• Audit Trails: Comprehensive logging of all agent interactions

🛣️ Roadmap

Phase 1: Core Platform (Current)

• ✅ ARTA rules engine with YAML configuration
• ✅ Basic MCP server implementation
• ✅ ML pipeline integration framework
• ⏳ Database connector development

Phase 2: Agent Orchestration (Q2 2025)

• 🔄 Multi-agent coordination framework
• 🔄 LangChain agent integration
• 🔄 MCP agent federation support
• 🔄 Real-time agent monitoring

Phase 3: Advanced Intelligence (Q3 2025)

• 📋 Advanced network analysis capabilities
• 📋 Federated learning across agent networks
• 📋 Automated agent discovery and registration
• 📋 Cross-domain knowledge transfer

Phase 4: Enterprise Scale (Q4 2025)

• 📋 High-availability deployment options
• 📋 Enterprise security and compliance features
• 📋 Advanced analytics and reporting
• 📋 Custom agent development framework

🤝 Contributing

We welcome contributions from the tactical AI and decision support community! See our for details on:

• 🔧 Rule Development: Creating domain-specific ARTA rules
• 🤖 Agent Integration: Adding new agent types and coordination patterns
• 📊 ML Models: Contributing specialized tactical analysis models
• 🛠️ Tools Development: Extending MCP tool capabilities

📜 License

This project is licensed under the MIT License - see the file for details.

🙏 Acknowledgments

• MAIF ARTA Project: For the foundational ARTA rules engine and adaptive analysis framework - https://maif.github.io/arta/
• MCP Protocol Team: For the foundational Model Context Protocol
• Open Source ML Community: For the models and tools that power our intelligence capabilities
• Strategic Decision Support Practitioners: For real-world validation and feedback

ARTA Tactical MCP Server - Transforming complex decisions through intelligent orchestration

For questions, support, or collaboration opportunities, please reach out to our team or open an issue in this repository.