🇪🇺 EU AI Act Risk Assessor

MCP server that classifies AI use cases under Regulation (EU) 2024/1689 with deterministic precision. Built with Vurb.ts.

Why This Exists

Every AI company in the EU needs to know: is my AI system legal?

The EU AI Act defines four risk tiers — from Unacceptable (banned) to Minimal (no obligations). Misclassification means fines up to €35 million or 7% of global revenue.

This MCP server gives any LLM client deterministic, auditable risk classification — zero hallucination on compliance.

Architecture

User describes AI use case (natural language)
        ↓
LLM translates → structured input (Zod-enforced enums/booleans)
        ↓
MCP applies legal matrix → deterministic verdict
        ↓
Presenter formats → rules, UI, suggested actions
        ↓
LLM presents result to user

LLM = Semantic Translator · MCP = Implacable Judge

The LLM handles what it does best (understanding language). The MCP handles what code does best (applying legal logic without drift).

Tools

| Tool | Description | |---|---| | risk.assess | Classify an AI use case across all four risk tiers | | fines.calculate | Calculate maximum fine exposure under Art. 99 | | exemptions.check | Interactive exemption auditor (guided decision tree) | | compliance.obligations | List all applicable obligations by risk level | | compliance.timeline | Enforcement deadlines and milestones | | compliance.generate_report | Generate Annex IV technical documentation template |

risk.assess

The main classification tool. The LLM maps the user's natural language description to structured fields:

Input:  "Our AI screens job applications and ranks candidates"
  ↓ LLM translates:
  inferred_domain: "employment"
  involves_profiling: true
  makes_autonomous_decisions: true
  ↓ MCP evaluates:
  → Art. 5(1)(f) match? No (no biometric data)
  → Annex III §4 match? Yes (employment + profiling)
  ↓
Output: HIGH RISK | Art. 6 + Annex III §4 | 7 obligations

exemptions.check

Transforms the LLM into a guided compliance interviewer:

Call 1: exemptions.check(risk_level: "high", {})
  → "Is this system purely procedural (no discretion)?"

Call 2: exemptions.check(risk_level: "high", { purely_procedural: false })
  → "Does it merely improve a prior human activity?"

Call 3: exemptions.check(risk_level: "high", { ..., improves_prior_activity: true })
  → EXEMPT under Art. 6(3)(b)

fines.calculate

Exact Art. 99 formula:

Prohibited practice:       max(€35M, 7% of global revenue)
High-risk noncompliance:   max(€15M, 3% of global revenue)
Incorrect information:     max(€7.5M, 1% of global revenue)

compliance.generate_report

Generates a complete Annex IV technical documentation template in Markdown — all 9 legally required sections with subsections. V8-safe (returns content as string, no filesystem access).

Legal Coverage

| Regulation Area | Articles | Implementation | |---|---|---| | Prohibited Practices | Art. 5(1)(a)–(h) | 8 rules with deterministic condition functions | | High-Risk Classification | Art. 6, Annex III | 8 domains, 15+ rules | | Limited Risk | Art. 50 | Transparency obligations | | Fines | Art. 99(3)–(5) | Exact formulas with SME proportionality | | Exemptions | Art. 6(3), Art. 2 | 6-node interactive decision tree | | Technical Documentation | Art. 11, Annex IV | 9-section template generator | | Enforcement Timeline | Arts. 111, 113 | Milestones with live status |

Usage Examples

🚫 Example 1 — Detecting a Prohibited Practice

User: "We're building an AI that analyses employee webcam footage to detect if they're stressed, bored, or engaged during meetings."

The LLM translates this into structured input and calls risk.assess:

{
  "intention_summary": "Real-time emotion recognition system analysing employee webcam footage during meetings",
  "inferred_domain": "employment",
  "processes_biometric_data": true,
  "involves_profiling": true,
  "makes_autonomous_decisions": false
}

Result: 🚫 UNACCEPTABLE — Art. 5(1)(f) prohibits emotion recognition in the workplace. This AI practice is illegal since 2 February 2025.

The Presenter automatically suggests fines.calculate and exemptions.check as next actions.

⚠️ Example 2 — High-Risk Classification (Employment)

User: "Our startup uses AI to screen CVs and shortlist the top 20% of candidates for interview."

{
  "intention_summary": "AI system that screens and ranks job applicants based on CV analysis",
  "inferred_domain": "employment",
  "processes_biometric_data": false,
  "involves_profiling": true,
  "makes_autonomous_decisions": true
}

Result: ⚠️ HIGH RISK — Annex III §4(a): AI systems used for recruitment and selection. Confidence: definitive. 7 compliance obligations apply.

The Presenter renders a Mermaid decision-flow diagram and suggests:

• compliance.obligations — review all required obligations
• fines.calculate — calculate maximum fine exposure
• compliance.generate_report — generate Annex IV documentation

⚠️ Example 3 — High-Risk Classification (Education)

User: "We want to build an AI that grades university exams automatically and decides who passes."

{
  "intention_summary": "Automated exam grading system with pass/fail decision authority",
  "inferred_domain": "education",
  "processes_biometric_data": false,
  "involves_profiling": true,
  "makes_autonomous_decisions": true
}

Result: ⚠️ HIGH RISK — Annex III §3(a): AI systems determining access to or outcomes in educational institutions.

ℹ️ Example 4 — Limited Risk (Chatbot)

User: "We have a customer support chatbot that answers questions about our products."

{
  "intention_summary": "Customer-facing chatbot for product FAQ and support queries",
  "inferred_domain": "none",
  "processes_biometric_data": false,
  "involves_profiling": false,
  "makes_autonomous_decisions": false
}

Result: ℹ️ LIMITED RISK — Art. 50: The system must clearly disclose to users that they are interacting with an AI. No further compliance obligations beyond transparency.

✅ Example 5 — Minimal Risk

User: "We use AI to recommend songs to users based on their listening history."

{
  "intention_summary": "Music recommendation engine based on listening patterns",
  "inferred_domain": "none",
  "processes_biometric_data": false,
  "involves_profiling": false,
  "makes_autonomous_decisions": false
}

Result: ✅ MINIMAL RISK — No specific obligations under the EU AI Act. Voluntary codes of conduct apply.

💰 Example 6 — Fine Calculation

User: "We're a company with €500M annual revenue. What's the worst-case fine if our prohibited AI system is caught?"

The LLM calls fines.calculate:

{
  "violation_type": "prohibited_practice",
  "global_annual_revenue_eur": 500000000,
  "is_sme": false
}

Result: | | Value | |---|---| | Fixed threshold | €35,000,000 | | Revenue-based (7%) | €35,000,000 | | Applicable fine | €35,000,000 | | Article | Article 99(3) |

If annual revenue were €1B, the revenue-based fine (€70M) would exceed the fixed threshold.

🔍 Example 7 — Interactive Exemption Audit

User: "Our AI was classified as high-risk, but it just automates a simple checklist — no real decisions."

The LLM initiates an interactive exemption audit via exemptions.check:

Round 1:

{ "risk_level": "high", "answers": {} }
→ "Is this AI system purely procedural, performing a narrow task with no discretionary judgement?"

User: "Yes, it just checks if all documents are present in a submission."

Round 2:

{ "risk_level": "high", "answers": { "purely_procedural": true } }
→ ✅ EXEMPT under Art. 6(3)(a) — The system performs a purely procedural task.

The Presenter rules change dynamically: instead of "ask the next question", it now shows the exemption details and advises legal verification.

📋 Example 8 — Full Compliance Workflow

A realistic end-to-end scenario:

Step 1 — User describes their system:

"We're building an AI for a city council that analyses CCTV footage to predict where crimes are likely to occur."

Step 2 — risk.assess classifies:

🚫 UNACCEPTABLE — Art. 5(1)(d): Predictive policing based on profiling.
   Domain: law_enforcement | Biometric: false | Profiling: true | Autonomous: true

Step 3 — User asks about exemptions:

"But what if it's for national security?"

Step 4 — exemptions.check evaluates:

→ "Is this system exclusively for military or national security purposes?"
User: "No, it's municipal police."
→ NOT EXEMPT — Art. 2(3) applies only to military/national security, not municipal law enforcement.

Step 5 — User asks about fines:

"What's the maximum fine for the city council?"

Step 6 — fines.calculate:

💰 Prohibited practice: max(€35M, 7% of global revenue)
   Public authorities typically face the fixed threshold: €35,000,000

Step 7 — User asks about the timeline:

"When does this become enforceable?"

Step 8 — compliance.timeline:

📅 Prohibited practices (Art. 5): ENFORCED since 2 February 2025
   ⚠️ This system is already illegal.

📄 Example 9 — Generating Technical Documentation

User: "Generate the Annex IV documentation template for our employment screening AI."

The LLM calls compliance.generate_report:

{
  "system_name": "TalentScreen AI",
  "system_description": "AI-powered CV screening and candidate ranking system",
  "intended_purpose": "Automated shortlisting of job applicants for interview",
  "domain": "employment",
  "provider_name": "Acme Corp"
}

Result: A complete Markdown document with all 9 Annex IV sections:

# Technical Documentation — TalentScreen AI
> Regulation (EU) 2024/1689 — Article 11, Annex IV

## 1. General Description of the AI System
## 2. Detailed Description of Elements and Development Process
## 3. Monitoring, Functioning and Control
## 4. Risk Management System
## 5. Data and Data Governance
## 6. Human Oversight Measures
## 7. Accuracy, Robustness and Cybersecurity
## 8. Transparency and Provision of Information
## 9. Post-Market Monitoring

Each section includes subsections with [To be completed by the provider] placeholders.

Deploy

Vinkius Cloud — One Command ⚡

The fastest path to production. vurb deploy publishes your server to Vinkius Cloud's global edge — zero infrastructure, built-in DLP, kill switch, audit logging, and a managed MCP token:

npm install
vurb deploy

That's it. No Dockerfile, no CI/CD pipeline, no servers to manage. You get a connection token that works with any MCP client — Cursor, Claude Desktop, Claude Code, Windsurf, Cline, VS Code + Copilot.

# Deploy with a custom name
vurb deploy --name eu-ai-act

# Deploy to a specific environment
vurb deploy --env production

💡 Tip: Install the Vinkius extension to manage your deployed server directly from VS Code, Cursor, or Windsurf — live connections, requests, P95 latency, DLP intercepts, token management, tool toggling, logs, and deployment history.

Connect Your MCP Client

After deploying, share the managed token with any MCP-compatible client:

Claude Desktop / Cursor / Windsurf

{
  "mcpServers": {
    "eu-ai-act": {
      "url": "https://edge.vinkius.com/your-token/mcp"
    }
  }
}

Self-Hosted Alternatives

The same ToolRegistry runs anywhere — no code changes required:

| Platform | Adapter | |---|---| | Vercel Edge Functions | @vurb/vercel | | Cloudflare Workers | @vurb/cloudflare | | Any Node.js server | Stdio / HTTP+SSE transport |

// Vercel — one line
import { vercelAdapter } from '@vurb/vercel';
export const POST = vercelAdapter({ registry, contextFactory });

// Cloudflare Workers — one line
import { cloudflareWorkersAdapter } from '@vurb/cloudflare';
export default cloudflareWorkersAdapter({ registry, contextFactory });

Full deployment guides: Production Server · Vercel Adapter · Cloudflare Adapter

Running Locally via stdio

If you prefer to run the server locally and connect via stdio — the native MCP transport used by Claude Desktop, Cursor, and Windsurf:

# 1. Install dependencies
npm install

# 2. Build
npm run build

# 3. Test it directly (optional)
node dist/server.js

Then add it to your MCP client config (e.g. claude_desktop_config.json):

{
  "mcpServers": {
    "eu-ai-act": {
      "command": "node",
      "args": ["/absolute/path/to/eu-ai-act/dist/server.js"]
    }
  }
}

Or run without cloning via npx (after the package is published):

{
  "mcpServers": {
    "eu-ai-act": {
      "command": "npx",
      "args": ["-y", "@mcp-originals/eu-ai-act"]
    }
  }
}

Note: The stdio transport is the default. No environment variables are required — the server runs fully offline with all legal data hardcoded.

Development

# Install dependencies
npm install

# Development server with HMR
vurb dev

# Type-check
npm run typecheck

# Run tests
npm test

Project Structure

src/
├── data/                 ← Legal knowledge base (hardcoded for determinism)
│   ├── types.ts
│   ├── prohibited-practices.ts    Art. 5
│   ├── annex-iii-matrix.ts        Annex III
│   ├── limited-risk.ts            Art. 50
│   ├── obligations.ts             Art. 9-15, 72
│   ├── timeline.ts                Enforcement dates
│   ├── exemptions.ts              Art. 6(3), Art. 2
│   ├── fines.ts                   Art. 99
│   └── annex-iv-template.ts       Documentation TOC
├── engine/
│   └── risk-engine.ts             Pure deterministic logic
├── models/                        M — defineModel()
│   └── index.ts
├── views/                         V — definePresenter()
│   └── index.ts
├── agents/                        A — Tool definitions
│   ├── risk/
│   │   └── assess.tool.ts
│   ├── fines/
│   │   └── calculate.tool.ts
│   ├── exemptions/
│   │   └── check.tool.ts
│   └── compliance/
│       ├── obligations.tool.ts
│       ├── timeline.tool.ts
│       └── generate-report.tool.ts
├── vurb.ts                        Shared Vurb instance
└── server.ts                      Bootstrap

Built with Vurb.ts

This server showcases the full Vurb.ts MVA (Model-View-Agent) pattern:

| Feature | Usage | |---|---| | defineModel() | 6 domain models with guidance labels as auto-rules | | definePresenter() | Schema validation, JIT rules, Mermaid diagrams, collection UI, suggested actions | | Semantic Verbs | f.query() (readOnly), f.action() (neutral) | | f.error() | Self-healing errors with suggestions | | .cached() | Static reference data (obligations, timeline) | | .instructions() | AI-first prompt engineering in the framework | | agentLimit | Cognitive guardrails with truncation guidance | | autoDiscover() | File-based tool routing |

Get started with Vurb.ts → · Documentation →

License

Apache 2.0 — See LICENSE for details.