CASA Certification MCP Server

A standalone Model Context Protocol (MCP) server for CASA Certification — CSOAI-Authorised Safety Assessment. This server provides comprehensive AI system certification tools with a 4-tier framework, Byzantine Council consensus mechanisms, and detailed compliance assessment.

Overview

CASA (CSOAI-Authorised Safety Assessment) is a rigorous framework for certifying AI systems with respect to safety, alignment, and responsible deployment. This MCP server operationalizes the CASA methodology by providing tools for assessment, gap analysis, Byzantine consensus simulation, roadmap planning, and audit preparation.

Key Features

• 4-Tier Certification Framework: T1 (Self-Assessment), T2 (Third-Party Audit), T3 (Continuous Monitoring), T4 (Byzantine Council)
• Comprehensive Assessment: Evaluates systems across 5 critical domains (Governance, Data, Model, Deployment, Monitoring)
• Byzantine Council Simulation: 33-LLM consensus voting with supermajority requirements
• Gap Analysis: Ranked gap identification with severity, effort, and cost estimates
• Certification Roadmaps: Phased timelines with milestones and resource requirements
• Audit Checklists: Domain-specific evidence requirements and tier-appropriate items
• Quick Scoring: Rapid compliance assessment from key questions

Installation

# Clone or download the server
cd /path/to/casa-certification

# Install dependencies
npm install

# Build TypeScript
npm run build

# Start the server
npm start

Architecture

Directory Structure

casa-certification/
├── src/
│   ├── index.ts                 # Main MCP server and tool handlers
│   ├── schemas.ts               # Zod validation schemas and types
│   ├── assessment.ts            # Full assessment logic
│   ├── gap-analysis.ts          # Gap analysis and prioritization
│   ├── byzantine.ts             # Byzantine Council simulation
│   ├── roadmap-checklist.ts     # Roadmap and checklist generation
│   └── resources.ts             # Knowledge resources and methodologies
├── package.json
├── tsconfig.json
└── README.md

Key Components

1. Assessment Engine (assessment.ts)

• Domain-based scoring (governance, data, model, deployment, monitoring)
• Risk-adjusted compliance calculations
• Tier determination based on total score
• Remediation step generation
• Timeline and cost estimation

2. Gap Analysis (gap-analysis.ts)

• Current state vs. target tier comparison
• Domain-specific gap identification
• Severity-based prioritization
• Effort and cost estimation
• Tier-specific requirements mapping

3. Byzantine Council (byzantine.ts)

• 33-judge diversity engine (specialties, orientations)
• Independent evaluation simulation
• Supermajority voting (22/33 required)
• Dissenting opinion capture
• Confidence score calculation

4. Roadmap Generation (roadmap-checklist.ts)

• Multi-phase implementation plans
• Resource allocation by phase
• Milestone definition and success criteria
• Cost breakdown and timeline adjustment
• Risk factor identification

5. Schema Validation (schemas.ts)

• Zod-based input validation
• Type-safe response structures
• Enum constraints for consistency
• Comprehensive type definitions

Tools

1. casa_full_assessment

Complete 4-tier CASA certification assessment providing comprehensive evaluation across all domains.

Input:

{
  aiSystemName: string;           // Name of the AI system
  description: string;             // Detailed system description
  sector: Sector;                  // Industry: healthcare, finance, autonomous-systems, etc.
  deploymentContext: string;       // Where/how deployed
  estimatedRiskLevel: RiskLevel;   // minimal, low, moderate, high, critical
}

Output:

{
  assessmentId: string;
  systemName: string;
  completionDate: string;
  tierRecommendation: CertificationTier;  // T1-T4
  complianceScore: number;         // 0-100
  gaps: Gap[];                     // Identified gaps with severity
  remediationSteps: string[];      // Phased remediation plan
  estimatedTimeline: string;       // Timeline to certification
  costRange: [number, number];     // Cost estimate
  confidenceLevel: string;         // low, medium, high
  nextSteps: string[];            // Recommended next actions
}

Example:

curl -X POST http://localhost:3000/tools/casa_full_assessment \
  -H "Content-Type: application/json" \
  -d '{
    "aiSystemName": "ContentGuardian LLM",
    "description": "Large language model for content moderation with multi-lingual support",
    "sector": "content-generation",
    "deploymentContext": "Cloud-based API serving 2M+ daily requests",
    "estimatedRiskLevel": "high"
  }'

2. casa_gap_analysis

Identify specific gaps between current practices and target certification tier.

Input:

{
  currentPractices: string;   // Description of current practices
  targetTier: CertificationTier;  // T1, T2, T3, or T4
}

Output:

Gap[]  // Array of gaps with:
// - id, area, description
// - severity (critical, high, medium, low)
// - remediationEffort (low, medium, high)
// - priority (numeric ranking)
// - estimatedHours, costRange

3. casa_byzantine_simulate

Simulate Byzantine Council consensus process with 33 independent LLM judges.

Input:

{
  assessmentData: Record<string, unknown>;  // Assessment metrics
  numJudges?: number;  // 3-99, default 33
}

Output:

{
  votes: Record<string, number>;  // approve, reject, abstain counts
  consensus: boolean;
  consensusDecision: string;
  supermajority: {
    achieved: boolean;
    votesFor: number;
    votesAgainst: number;
    abstained: number;
    required: number;  // 22/33 for T4
  };
  confidenceScore: number;  // 0-100
  dissentingOpinions: string[];  // Top dissenting views
  deliberationNotes: string[];  // Sample of judge reasoning
}

4. casa_certification_roadmap

Generate phased certification roadmap with timelines and resource requirements.

Input:

{
  organizationSize: "small" | "medium" | "large" | "enterprise";
  sector: Sector;
  currentMaturityLevel: "initial" | "developing" | "managed" | "optimized";
  targetTier: CertificationTier;
  timelinePreference: "accelerated" | "standard" | "extended";
}

Output:

{
  organizationProfile: {...};
  totalDuration: string;  // "24 weeks (approximately 6 months)"
  totalEstimatedCost: [number, number];
  phases: RoadmapPhase[];  // Each with milestones, activities, criteria
  riskFactors: string[];
  criticalSuccessFactors: string[];
}

5. casa_audit_checklist

Generate audit preparation checklist with evidence requirements.

Input:

{
  tier: CertificationTier;
  sector: Sector;
  aiSystemType: string;  // LLM, recommendation engine, etc.
}

Output:

{
  tier: string;
  sector: string;
  systemType: string;
  generatedDate: string;
  sections: {
    [domain: string]: ChecklistItem[];  // governance, data, model, etc.
  };
  estimatedCompletionHours: number;
  totalItems: number;
}

6. casa_score

Quick compliance scoring from key assessment questions.

Input:

{
  answers: Record<string, string | number | boolean>;
  // Example keys: governance_board, data_lineage, safety_testing, etc.
}

Output:

{
  score: number;  // 0-100
  percentage: string;
  qualifyingTier: CertificationTier;
  gaps: Array<{area: string; gap: string}>;  // Top 3
  recommendations: string[];
}

Resources

The server provides three key resources accessible via the MCP resource protocol:

casa://methodology

Complete CASA methodology documentation including:

• Core principles (risk-based, transparency, continuous assurance)
• Assessment domains and scoring methodology
• Tier advancement criteria

casa://tiers

Detailed tier definitions:

• T1: Self-Assessment (40+ points, 6-month validity)
• T2: Third-Party Audit (65+ points, 12-month validity)
• T3: Continuous Monitoring (80+ points, 18-month validity)
• T4: Byzantine Council Review (90+ points, 24-month validity)

casa://pricing

CASA pricing structure:

• Tier-specific cost ranges
• Volume discounts
• Academic/non-profit rates
• Payment terms and additional services

Certification Tiers

Tier 1: Self-Assessment (T1)

• Minimum Score: 40/100
• Duration: 2-6 weeks
• Cost: $5,000 - $15,000
• Validity: 6 months
• Process: Internal self-assessment with structured guidance

Tier 2: Third-Party Audit (T2)

• Minimum Score: 65/100
• Duration: 8-16 weeks
• Cost: $25,000 - $75,000
• Validity: 12 months
• Process: CASA-accredited auditor conducts comprehensive evaluation

Tier 3: Continuous Monitoring (T3)

• Minimum Score: 80/100
• Duration: 12-20 weeks initial, ongoing monitoring
• Cost: $75,000 - $200,000/year
• Validity: 18 months (with continuous monitoring)
• Process: T2 audit + real-time monitoring + quarterly reassessments

Tier 4: Byzantine Council Review (T4)

• Minimum Score: 90/100
• Duration: 20-32 weeks
• Cost: $200,000 - $500,000+
• Validity: 24 months (with continuous monitoring)
• Process: 33-judge panel with 22/33 supermajority requirement

Assessment Domains

All assessments evaluate five critical domains:

1. Governance (20 points)

   • Safety review board, incident response, compliance tracking

2. Data (20 points)

   • Data lineage, quality assurance, bias mitigation, privacy

3. Model (20 points)

   • Training methodology, safety testing, failure analysis, alignment

4. Deployment (20 points)

   • Monitoring, access controls, safeguards, audit logging

5. Monitoring (20 points)

   • Performance tracking, anomaly detection, reassessment schedule

Usage Examples

Quick Assessment of System

# Generate quick compliance score
curl -X POST http://localhost:3000/tools/casa_score \
  -H "Content-Type: application/json" \
  -d '{
    "answers": {
      "governance_board": true,
      "data_lineage": true,
      "safety_testing": false,
      "deployment_monitoring": true,
      "continuous_monitoring": false
    }
  }'

Full Assessment for AI System

# Run comprehensive assessment
curl -X POST http://localhost:3000/tools/casa_full_assessment \
  -H "Content-Type: application/json" \
  -d '{
    "aiSystemName": "ContentGuardian LLM",
    "description": "Multi-lingual content moderation system using fine-tuned LLM",
    "sector": "content-generation",
    "deploymentContext": "Cloud API, production, 2M daily requests",
    "estimatedRiskLevel": "high"
  }'

Generate Certification Roadmap

# Get phased implementation plan
curl -X POST http://localhost:3000/tools/casa_certification_roadmap \
  -H "Content-Type: application/json" \
  -d '{
    "organizationSize": "large",
    "sector": "finance",
    "currentMaturityLevel": "developing",
    "targetTier": "T3",
    "timelinePreference": "standard"
  }'

Simulate Byzantine Council

# Test consensus mechanism
curl -X POST http://localhost:3000/tools/casa_byzantine_simulate \
  -H "Content-Type: application/json" \
  -d '{
    "assessmentData": {
      "complianceScore": 92,
      "riskLevel": "moderate",
      "incidentHistory": "clean"
    },
    "numJudges": 33
  }'

Validation

All inputs are validated using Zod schemas. Validation errors provide clear feedback:

// Invalid input
{
  "aiSystemName": "",  // Too short
  "description": "Short",  // Too short
  "sector": "invalid",  // Invalid enum
  "deploymentContext": "Cloud",  // Too short
  "estimatedRiskLevel": "unknown"  // Invalid enum
}

// Returns error with details on each field

Error Handling

The server handles errors gracefully:

{
  "error": "Validation error: aiSystemName is required",
  "details": {
    "field": "aiSystemName",
    "message": "String must contain at least 1 character(s)"
  }
}

Development

Build

npm run build

Watch Mode

npm run watch

Development Server

npm run dev

Type Checking

npm run build  # Includes type checking

Testing Scenarios

Scenario 1: Early-Stage Startup

# Assessment for new AI company
POST /tools/casa_full_assessment
{
  "aiSystemName": "NewAI ChatBot",
  "description": "Conversational AI for customer support",
  "sector": "other",
  "deploymentContext": "Internal testing",
  "estimatedRiskLevel": "low"
}

# Expected: T1 recommendation, 45-55 compliance score

Scenario 2: Regulated Industry

# Assessment for healthcare AI
POST /tools/casa_full_assessment
{
  "aiSystemName": "MediDiagnose",
  "description": "Medical imaging analysis with FDA requirements",
  "sector": "healthcare",
  "deploymentContext": "Hospital systems, clinical decision support",
  "estimatedRiskLevel": "critical"
}

# Expected: T3/T4 recommendation, 75+ compliance score required

Scenario 3: Compliance Journey

# Identify gaps for T3 certification
POST /tools/casa_gap_analysis
{
  "currentPractices": "We have basic monitoring and incident response",
  "targetTier": "T3"
}

# Then generate roadmap
POST /tools/casa_certification_roadmap
{
  "organizationSize": "medium",
  "sector": "finance",
  "currentMaturityLevel": "managed",
  "targetTier": "T3",
  "timelinePreference": "standard"
}

Configuration

Environment variables (optional):

# Server binding
MCP_SERVER_HOST=localhost
MCP_SERVER_PORT=3000

# Resource paths
CASA_RESOURCES_PATH=./resources

# Logging
LOG_LEVEL=info  # debug, info, warn, error

Performance Considerations

• Assessment Generation: ~100-500ms (depends on system complexity)
• Byzantine Simulation: ~200-1000ms (scales with judge count)
• Roadmap Generation: ~100-300ms
• Gap Analysis: ~50-200ms

All tools are stateless and can handle concurrent requests.

Security

• No external API calls or network dependencies
• All computation is local
• No data persistence or logging
• Zod validation prevents injection attacks
• Type-safe TypeScript throughout

Contributing

This is a reference implementation of the CASA methodology. Contributions welcome:

1. Fork the repository
2. Create a feature branch
3. Submit a pull request with tests

License

CC0-1.0 — Public Domain. See LICENSE file.

Support

For questions about CASA methodology:

• Visit: https://csoai.org/casa
• Contact: [email protected]

For MCP server issues:

• GitHub Issues: [project-repo]/issues
• Documentation: [project-repo]/docs

Changelog

v1.0.0 (Initial Release)

• Full MCP server implementation
• 6 core tools (full assessment, gap analysis, Byzantine simulation, roadmap, checklist, quick score)
• 3 knowledge resources (methodology, tiers, pricing)
• Zod validation on all inputs
• TypeScript strict mode
• Production-ready code quality

Acknowledgments

CASA framework developed by the Council for the Safety of Artificial Intelligence (CSOAI).

This MCP implementation follows best practices from the Model Context Protocol specification.