AI Economy Infrastructure MCP

The unified orchestration layer for the entire CSOAI ecosystem. This is the master META MCP server that sits on top of all 10 specialist MCP servers, providing cross-ecosystem intelligence, routing, aggregation, and unified governance capabilities.

Overview

The AI Economy Infrastructure MCP serves as the "brain" of the CSOAI ecosystem, coordinating across 10 specialist servers to provide:

• Intelligent Routing: Query-aware routing to the most relevant specialist MCPs
• Unified Governance: Combined AI governance and risk assessment across frameworks
• Sector Compliance: Sector-specific compliance packages and roadmaps
• Cross-Ecosystem Intelligence: Aggregated metrics, trust scoring, and analytics
• Education Integration: Learning pathways feeding into OneOS MOOC platform
• Incident Coordination: Multi-domain incident response with specialist coordination
• Certification Bundling: Unified certification pathways with single point of contact

Architecture

┌─────────────────────────────────────────────────────────────────┐
│           AI Economy Infrastructure MCP (Master)                │
│                                                                  │
│  10 Tools + 4 Resources + Intelligent Orchestration             │
└────────────┬────────────────────────────────────────────────────┘
             │
    ┌────────┴────────┬──────────┬──────────┬──────────┬──────────┐
    │                 │          │          │          │          │
┌───▼──┐  ┌────────┐  │  ┌─────┐│ ┌──────┐ │┌──────┐ │┌───────┐│
│CSOAI │  │ CASA   │  │  │CSGA ││ │Quantra││PROOFOF ││BMCC   ││
│ Gvn  │  │Cert    │  │  │Cyber││ │Quantum││Verif  ││Cyber  ││
└──────┘  └────────┘  │  └─────┘│ └──────┘ │└──────┘ │└───────┘│
    │         │       │    │     │    │    │    │    │    │
    │         │       │    │     │    │    │    │    │    │
┌───▼──┐  ┌──▼────┐  │  ┌──▼───┐│ ┌──▼───┐│┌──▼──┐ ││
│OneOS │  │K.A.T.A│  │  │CSO   ││ │DAO   │││    ││
│MOOC  │  │Belts  │  │  │Pharma││ │Defence│││    ││
└──────┘  └───────┘  │  └──────┘│ └──────┘│└────┘ ││
                     │          │          │        │
                     └──────────┴──────────┴────────┘

10 Core Tools

1. ai_economy_router

Intelligent request routing to specialist MCPs based on natural language queries.

Input:

{
  "query": "What's my governance compliance status?",
  "context": {"sector": "aifinance", "organization_id": "org_123"}
}

Output:

• Primary recommended MCPs with reasoning
• Secondary MCPs for cross-domain queries
• Recommended execution sequence
• Aggregation strategy (parallel/sequential/hierarchical)
• Expected outputs from each MCP

2. ai_governance_assess

Unified AI governance assessment combining CSOAI frameworks and CASA certification.

Input:

{
  "ai_system_description": "ML-based credit scoring system for financial lending",
  "sector": "aifinance",
  "jurisdiction": "EU",
  "deployment_type": "cloud"
}

Output:

• Risk classification (critical/high/medium/low/minimal)
• Applicable crosswalks (AI Act, GDPR-AI, CEP, etc.)
• CASA tier recommendation (1-5)
• Compliance gaps with severity levels
• Certification pathway with timeline
• Cost estimate (USD)

3. ai_sector_compliance

Sector-branded compliance packages for 8 supported sectors.

Supported Sectors:

• aigovernance - Core AI governance
• aifinance - Financial services AI
• aiinsurance - Insurance and risk
• aihealthcare - Healthcare/pharma
• aidefence - Defence/military
• aieducation - Education
• aimanufacturing - Manufacturing/industrial
• aienergy - Energy and utilities

Input:

{
  "sector": "aihealthcare",
  "organization_size": "large",
  "current_compliance": ["HIPAA", "FDA CFR Part 11"],
  "jurisdiction": "US"
}

Output:

• Compliance bundle ID
• Required standards list
• Applicable certifications with timelines and costs
• Sector-specific requirements with deadlines
• Phased compliance roadmap

4. ai_economy_dashboard

Real-time cross-ecosystem analytics and intelligence.

Input:

{
  "organization_id": "org_456",
  "date_range": {
    "start_date": "2026-01-01T00:00:00Z",
    "end_date": "2026-02-25T23:59:59Z"
  },
  "metrics": ["compliance", "security", "learning"]
}

Output:

• Cross-ecosystem usage metrics (per MCP)
• Compliance posture score (0-100)
• Learning progress percentage
• Security status (secure/at_risk/critical)
• PQC readiness score
• Prioritized recommended actions

5. ai_trust_score

Unified AI trust scoring from all ecosystem signals.

Input:

{
  "ai_system_name": "AdvancedLenderAI",
  "organization": "FinTech Corp",
  "deployment_context": "CASA Tier 3, cloud deployment, encryption enabled"
}

Output:

• Composite trust score (0-100)
• Governance compliance score
• Security posture score
• PQC readiness score
• Verification status (verified/pending/unverified)
• Training completion percentage
• Risk flags and recommendations

6. ai_learning_pathway

Personalized cross-ecosystem learning recommendations.

Input:

{
  "role": "AI Governance Lead",
  "skill_gaps_identified": ["advanced governance", "CASA Tier 4", "quantum threats"],
  "sector": "aifinance",
  "career_goals": "CASA certification lead within 12 months"
}

Output:

• Personalized learning pathway ID
• Recommended courses (OneOS MOOC, BMCC Cyber, CSGA, etc.)
• K.A.T.A. belt progression plan
• CASA certification prep track
• OneOS enrollment hooks for auto-enrollment and tracking

7. ai_data_pipeline

Data collection configuration for MOOC and analytics integration.

Input:

{
  "data_sources": ["csoai-governance", "casa-certification", "bmcc-cyber"],
  "metrics_wanted": ["assessments", "certifications", "training_completions"],
  "aggregation_period": "daily"
}

Output:

• Pipeline configuration with source specifications
• Collection status (active/configured/error)
• Aggregated insights from all data sources
• MOOC integration configuration and sync status

8. ai_market_intelligence

Cross-ecosystem market analysis and opportunity assessment.

Input:

{
  "sector": "aihealthcare",
  "geography": "EU",
  "time_horizon": "3years"
}

Output:

• Market size (USD millions)
• Market growth rate (%)
• Regulatory landscape by jurisdiction
• Competitor activity and threat levels
• Opportunity assessment with market sizes
• Recommended CSOAI services

9. ai_incident_command

Coordinated incident response across specialist MCPs.

Supported Incident Types:

• cyber - Cybersecurity breaches
• ai_safety - AI system safety failures
• compliance - Regulatory/compliance violations
• quantum_threat - Quantum computing threats
• multi_domain - Cross-domain incidents

Input:

{
  "incident_type": "cyber",
  "severity": "critical",
  "affected_systems": ["credit_scoring_ai", "fraud_detection_ai"],
  "description": "Unauthorized access detected on production ML models..."
}

Output:

• Incident ID and unified response plan
• Coordinated actions across MCPs
• Phase-by-phase action items with timelines
• Escalation path by severity
• Communication protocol

10. ai_certification_bundle

Multi-certification pathway bundling CASA, K.A.T.A., CSR5, and PQC.

Input:

{
  "organization_profile": {
    "name": "RegulatedAI Inc",
    "sector": "aifinance",
    "size": "large",
    "jurisdiction": "US"
  },
  "target_certifications": ["CASA Tier 4", "CSGA Level 2", "PQC Readiness"],
  "timeline_months": 18
}

Output:

• Bundle ID with all certifications
• Unified timeline (months)
• Critical path with phase-by-phase breakdown
• Single point of contact info
• Combined pricing with bundle discount
• Payment terms

4 Core Resources

1. ai-economy://ecosystem

Complete ecosystem topology and cross-integration mapping.

Includes:

• All 10 specialist MCP registries
• Capabilities per MCP
• Sector coverage
• Cross-ecosystem routing map
• Data integration points

2. ai-economy://sectors

Supported sectors and compliance frameworks.

Includes:

• 8 supported sectors
• MCPs per sector
• Jurisdiction-specific crosswalks
• Sector requirements

3. ai-economy://trust-framework

AI trust scoring methodology and interpretation.

Includes:

• Scoring dimensions and weights
• Score range interpretations
• Risk level classifications
• Sub-criteria definitions

4. ai-economy://data-schema

Data collection schema for OneOS MOOC integration.

Includes:

• Collections and field definitions
• MOOC enrollment schema
• Progress tracking schema
• Assessment schema

Setup & Installation

Prerequisites

• Node.js 18+
• npm or yarn

Install

cd /sessions/brave-adoring-cerf/mcp-servers/ai-economy-infrastructure
npm install

Build

npm run build

Run

npm run dev

Or in production:

npm start

Development

Type Checking

npm run lint

Clean Build

npm run clean && npm run build

Architecture Principles

1. Non-Replacing Orchestration

This MCP does NOT replace the 10 specialist servers. Instead, it:

• Knows which specialist to call for which queries
• Aggregates results when multiple specialists are needed
• Provides unified interfaces over specialist capabilities

2. Intelligent Routing

Router uses:

• Natural language query analysis
• Keyword-based specialist matching
• Sector and context-aware selection
• Graph-based cross-ecosystem mapping

3. Data Integration Hub

Acts as central nervous system:

• Collects data from all 10 specialists
• Aggregates into ecosystem dashboards
• Feeds OneOS MOOC with enrollment/progress data
• Maintains cross-ecosystem data schemas

4. Single Point of Contact

For organizations:

• One unified interface for entire ecosystem
• One certification pathway coordinator
• One incident response coordinator
• Unified billing and contracts

Usage Examples

Example 1: Unified Governance Assessment

curl -X POST http://localhost:3000/tools/ai_governance_assess \
  -H "Content-Type: application/json" \
  -d '{
    "ai_system_description": "Autonomous vehicle decision-making system",
    "sector": "aimanufacturing",
    "jurisdiction": "EU",
    "deployment_type": "edge"
  }'

Example 2: Intelligent Routing

curl -X POST http://localhost:3000/tools/ai_economy_router \
  -H "Content-Type: application/json" \
  -d '{
    "query": "What cybersecurity threats do we face with quantum computing?",
    "context": {"sector": "aifinance"}
  }'

Example 3: Multi-Certification Bundle

curl -X POST http://localhost:3000/tools/ai_certification_bundle \
  -H "Content-Type: application/json" \
  -d '{
    "organization_profile": {
      "name": "Healthcare AI Solutions",
      "sector": "aihealthcare",
      "size": "enterprise",
      "jurisdiction": "US"
    },
    "target_certifications": ["CASA Tier 4", "CSO Pharma-AI", "PQC Readiness"],
    "timeline_months": 24
  }'

Ecosystem Integration Points

CSOAI Governance ↔ CASA Certification

• Risk assessments inform tier recommendations
• Tier progression drives governance reassessment

CSGA Cybersecurity ↔ QuantraNet

• Security assessments identify crypto weaknesses
• PQC readiness feeds into security strategy

BMCC Cyber ↔ OneOS MOOC

• Training enrollment syncs to MOOC
• Completion tracking flows back to BMCC
• K.A.T.A. belts grant course prerequisite wavers

PROOFOF ↔ All MCPs

• Content verification ensures data integrity
• Provenance chains track compliance documentation

CSO Pharma ↔ CSOAI + CASA

• Healthcare regulations mapped to governance frameworks
• Clinical validation requirements inform tier assessment

DAO Defence ↔ CSGA + Quantra

• Defence regulations drive security requirements
• Quantum threat assessment mandatory for classified systems

Key Metrics

The ecosystem tracks:

• Compliance Posture Score: 0-100 aggregate
• Learning Progress: Percentage across MOOC/K.A.T.A.
• Trust Score: Composite from 5 dimensions
• Security Status: Secure/At-Risk/Critical
• PQC Readiness: 0-100 percent
• Certification Pipeline: Tier progression tracking

Data Privacy & Security

• All organization data encrypted in transit and at rest
• No PII stored without explicit consent
• Data isolation between organizations
• Audit trails for all governance decisions
• GDPR, HIPAA, and sector compliance built-in

Support & Maintenance

• Server version: 1.0.0
• License: CC0-1.0
• Author: CSOAI — Council for the Safety of Artificial Intelligence
• Homepage: https://csoai.org

Contributing

This is the master orchestration layer. Contributions should focus on:

• Router algorithm improvements
• New aggregation strategies
• Cross-ecosystem route optimizations
• Additional resource schemas

Specialist MCP enhancements should be contributed to the respective specialist repositories.

This is the crown jewel of the CSOAI ecosystem. It represents the unified intelligence layer that makes governance, compliance, certification, education, and security a coordinated, enterprise-scale operation across all AI governance domains.