mcp-eu-ai-act
@eucomplyhub/mcp-eu-ai-act
MCP (Model Context Protocol) server exposing free EU AI Act compliance classifiers to AI assistants. Built for Claude Desktop, Cursor, Windsurf, and any MCP-compatible client.
Powers AI assistants to evaluate mid-market SaaS systems against EU AI Act, NIST AI RMF, ISO/IEC 42001, OECD, GDPR, and sector-specific overlays.
⚡ TL;DR (60-second install)
npm install -g @eucomplyhub/mcp-eu-ai-actAdd to your Claude Desktop config (~/Library/Application Support/Claude/claude_desktop_config.json):
{
"mcpServers": {
"eucomplyhub": {
"command": "npx",
"args": ["-y", "@eucomplyhub/mcp-eu-ai-act"]
}
}
}Restart Claude Desktop. Ask:
"Classify Acme Inc — an HR-tech SaaS using AI to score candidates for B2B enterprise customers."
Claude returns full Annex III mapping, Article 50 transparency obligations, GPAI Article 53 reasoning, and 5 priority remediation actions tailored to the deployment.
Related MCP server: eu-ai-act-mcp
🆚 How does this differ from other EU AI Act MCP servers?
There's one other EU AI Act MCP server in the awesome-mcp-servers Legal section (@ark-forge/mcp-eu-ai-act). It's a different scope:
This server | ark-forge/mcp-eu-ai-act | |
Frameworks | EU AI Act + NIST AI RMF + ISO/IEC 42001 + OECD + Singapore + GDPR + HIPAA + Colorado SB 24-205 | EU AI Act only |
Layers | Two: deterministic 30-sec + Claude-powered 60-sec | Code scanner |
Output | Multi-jurisdiction risk class + Annex III mapping + Article 50/53 reasoning + 5 remediation actions | Violations + remediation guidance |
Use case | Mid-market SaaS audit prep across multiple frameworks | Codebase compliance check |
Use whichever fits your scope. They're complementary, not competitive.
🎬 Sample Claude Desktop output
> Classify Acme Inc — HR-tech SaaS using AI to score candidates for B2B enterprise customers.
Running classify_annex3...
Overall risk: HIGH-RISK
Summary: Acme operates in Annex III §4 (employment), with AI-driven
candidate scoring affecting hiring decisions. Article 50 disclosure
applies. EU customers trigger full obligations.
Annex III categories:
III.4 Employment ✓ YES — AI ranking influences hiring
III.5 Essential services — NO — not credit/benefit scoring
[...6 more categories...]
Article 50: APPLIES
Reasoning: Candidates interact with AI-generated outputs;
transparency disclosure required at the point of evaluation.
GPAI Article 53: deployer (you consume third-party foundation model)
Reasoning: Score generation via OpenAI/Anthropic API → you're
not the provider, but Article 26 deployer obligations apply.
Priority remediation actions:
1. Implement Article 14 human oversight UI — manager confirmation
step before AI-ranked candidates auto-proceed
2. Add Article 50 transparency banner — "AI-assisted scoring"
notice visible to candidates
3. Document training data lineage (Article 10) — if Acme fine-tunes
4. Establish post-market monitoring (Article 72)
5. GDPR overlap — DPIA required (Annex III high-risk = Art 35 trigger)Tools exposed
1. quick_risk_class — 30-second multi-jurisdiction risk classifier
Deterministic, rule-based AI risk classification. No LLM call — same inputs always produce the same outputs.
Frameworks covered:
🇪🇺 EU AI Act (Articles 5, 9–15, 26, 27, 50, 53)
🇺🇸 NIST AI RMF (Govern · Map · Measure · Manage)
🌐 ISO/IEC 42001 (Clauses 4–10 + Annex B)
🌍 OECD AI Principles
🇸🇬 Singapore Model AI Governance
🇨🇳 PRC GenAI Interim Measures (for generation archetype)
🔐 GDPR + UK GDPR + DPDP + CCPA + LGPD + PIPEDA (privacy stack)
🏥 HIPAA / FDA SaMD / EU MDR-AI (healthcare overlay)
⚖️ EEOC + NYC AEDT + Colorado SB 24-205 (US employment overlay)
🏛️ OMB M-24-10 + CoE AI Convention (public sector overlay)
Inputs:
industry: healthcare, publicSector, education, hr, retail, industrial, media, otherarchetype: decisioning, generation, classification, recommendation, automation, forecastingimpact: internal, b2b, consumer, regulated
Returns: Risk class (Critical / High / Limited / Minimal), 5-axis risk profile, per-framework verdicts.
2. classify_annex3 — Deep Annex III classification (Claude-powered)
Full EU AI Act mapping using Claude (Anthropic) with complete regulatory context — Articles 6, 9–15, 26, 27, 50, 53 plus the postponement nuance (Annex III standalone enforcement postponed to Dec 2 2027; Article 50 + GPAI Article 53 lock in Aug 2 2026).
Inputs:
company: Company nameindustry: Industry/verticalfeatures: Array of AI featuresuseCase: Plain-English description (min 20 chars)euExposure: eu-customers-output, eu-employees-only, no-eu, considering-eu
Returns:
Overall risk classification (high-risk / limited-risk / gpai / minimal-risk)
8 Annex III categories with applies status + reasoning
Article 50 transparency obligations + reasoning
GPAI Article 53 applicability (provider / deployer) + reasoning
5 priority remediation actions tailored to your stack
Calls https://eucomplyhub.com/api/annex3-classify — free, no signup, ~60s response time.
Install
For Claude Desktop
Install the package globally:
npm install -g @eucomplyhub/mcp-eu-ai-actEdit your Claude Desktop config file:
macOS:
~/Library/Application Support/Claude/claude_desktop_config.jsonWindows:
%APPDATA%\Claude\claude_desktop_config.json
Add the server:
{ "mcpServers": { "eucomplyhub": { "command": "npx", "args": ["-y", "@eucomplyhub/mcp-eu-ai-act"] } } }Restart Claude Desktop.
In a new chat, ask:
Use the eucomplyhub tools to classify Acme Inc — an HR-tech SaaS with CV screening features for European enterprise customers.
Claude will call
classify_annex3and return a structured Annex III mapping + priority remediation actions.
For Cursor
Add to .cursor/mcp.json (workspace) or ~/.cursor/mcp.json (global):
{
"mcpServers": {
"eucomplyhub": {
"command": "npx",
"args": ["-y", "@eucomplyhub/mcp-eu-ai-act"]
}
}
}For Windsurf
Add to ~/.codeium/windsurf/mcp_config.json:
{
"mcpServers": {
"eucomplyhub": {
"command": "npx",
"args": ["-y", "@eucomplyhub/mcp-eu-ai-act"]
}
}
}Generic stdio client
npx -y @eucomplyhub/mcp-eu-ai-actThe server runs over stdio (standard MCP transport).
Example prompts
Once installed, ask your AI assistant questions like:
"What's the EU AI Act risk class for an HR-tech SaaS using AI to score job candidates?"
"Run the quick risk classifier for a healthcare diagnostic AI used by EU regulated medical providers."
"Deep-classify Acme Inc — they're a B2B fintech using AI to score loan applicants in the EU."
"For Notion AI features, what Annex III categories apply and what's the Article 50 obligation?"
The assistant will call the appropriate tool and return structured results you can act on.
What does this cost?
Free. Both tools call free public endpoints at eucomplyhub.com:
quick_risk_classruns entirely locally (deterministic JavaScript, no network call)classify_annex3callshttps://eucomplyhub.com/api/annex3-classify(rate-limited free public API)
Rate limits apply for abuse prevention. Heavy usage should consider commissioning a full audit via eucomplyhub.com/audit.
Disclaimer
This MCP server is an educational tool for orientation, not legal advice. Each framework has specific clauses, exceptions, and edge cases. For binding compliance mapping (audit deliverables, certification prep, regulatory submission), consult an expert.
For a paid expert audit:
📋 Tier 1 Quick Audit (€799): Free /risk-class + /annex3 + 60-min consultation
🔬 Tier 2 Full Audit (€1,999): Triple-framework methodology + audit-ready deliverable
🛡️ Tier 3 Continuous Monitoring (€299/mo): Post-audit ongoing review
Book at eucomplyhub.com/audit.
Methodology
Built and maintained by Piotr Reder (eucomplyhub.com). Triple-framework specialist for mid-market SaaS preparing for EU AI Act enforcement.
Risk class logic adapted from @clustral/risk-compass (MIT).
Annex III deep classifier powered by Anthropic Claude (claude-sonnet-4-6).
Web versions
Prefer a browser?
🌐 eucomplyhub.com/risk-class — interactive Tier 0 classifier (same logic as
quick_risk_class)🔬 eucomplyhub.com/annex3 — Tier 1 deep classifier (same as
classify_annex3)🗺️ eucomplyhub.com/crosswalk — interactive EU AI Act ↔ ISO 42001 ↔ NIST mapping
License
MIT — see LICENSE.
Contributing
Issues + PRs welcome at https://github.com/eucomplyhub/mcp-eu-ai-act
For questions about the audit methodology behind the tools, reach out: piotr@eucomplyhub.com
Roadmap
Q3 2026 (July–September)
classify_fria— Fundamental Rights Impact Assessment generator (Article 27)classify_hr_bias— Vertical bias audit for HR-tech (Annex III §4 + Colorado SB 24-205)validate_disclosure— Article 50 transparency UX audit (Generated by AI label checker)
Q4 2026 (October–December)
audit_gpai_provider— Article 53 GPAI provider compliance check (training data lineage + technical documentation)monitor_continuous— Post-market monitoring helper (Article 72)Localized output: PL, DE, FR, ES, IT
2027
Annex III standalone enforcement support (Dec 2 2027 deadline)
ISO/IEC 42001 certification readiness audit module
Feedback on priorities? Open an issue or email piotr@eucomplyhub.com.
Changelog
0.1.0 — 2026-05-14
Initial release
Two tools:
quick_risk_class+classify_annex310+ frameworks covered (EU AI Act, NIST AI RMF, ISO/IEC 42001, OECD, Singapore, GDPR, HIPAA, Colorado SB 24-205)
Claude Desktop / Cursor / Windsurf install instructions
Multi-jurisdiction overlay logic
Postponement-aware (Annex III standalone → Dec 2 2027; Article 50 + GPAI 53 stay Aug 2 2026)
Available Tools
2 toolsclassify_annex3A
Deep Annex III classification using Claude (Anthropic) with full EU AI Act regulatory context. Maps your product against all 8 Annex III high-risk categories + Article 50 transparency + GPAI Article 53 + GPAI provider/deployer reasoning. Returns 5 priority remediation actions tailored to your stack. ~60 seconds (LLM-powered).
| Name | Required | Description | Default |
|---|---|---|---|
| company | Yes | Company name being classified. | |
| industry | Yes | Industry/vertical (e.g., 'HR-tech', 'fintech', 'healthtech', 'productivity', 'voice-AI'). | |
| features | Yes | AI features in the product. Examples: 'content-generation', 'scoring', 'decision-making', 'api-consumer', 'own-model', 'recommendation', 'classification'. | |
| useCase | Yes | Plain-English description of what the AI does and who it serves. Min 20 chars. | |
| euExposure | Yes | EU exposure level. 'eu-customers-output' = serves EU customers with AI-influenced outputs. 'eu-employees-only' = internal tools used by EU employees only. 'no-eu' = no EU footprint. 'considering-eu' = planning EU expansion. |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations exist, so the description carries the full burden. It discloses that the tool is 'LLM-powered' and takes ~60 seconds, but does not mention side effects, authentication needs, or data handling beyond the input schema. It provides moderate transparency.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is concise (three sentences) with no wasted words. It front-loads the purpose, then adds detail, and ends with timing. Every sentence adds value.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the complexity of EU AI Act classification, the description covers the scope (categories, articles), input (via schema), and output (5 remediation actions). No output schema, but it specifies what is returned. Could mention output format explicitly.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%—every parameter has a description. The tool description does not add additional meaning beyond the schema for individual parameters. Baseline 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states it performs 'Deep Annex III classification' using Claude, mapping against specific regulatory categories (8 Annex III, Article 50, GPAI), and returns remediation actions. This distinguishes it from the sibling 'quick_risk_class' which is likely a lighter alternative.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies it is a deep, time-consuming (~60 seconds) tool but does not explicitly state when to use it over 'quick_risk_class' or when not to use it. Usage guidance is implicit rather than explicit.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
quick_risk_classA
30-second deterministic multi-jurisdiction AI risk classification across EU AI Act, NIST AI RMF, ISO/IEC 42001, OECD, Singapore Model AI Governance, GDPR, and sector-specific overlays (HIPAA, NYC AEDT, Colorado SB 24-205). No LLM call. Same inputs always produce same outputs. Use this for quick orientation before deeper analysis.
| Name | Required | Description | Default |
|---|---|---|---|
| industry | Yes | Industry vertical. Use 'other' for fintech/banking/insurance. | |
| archetype | Yes | What the AI primarily does: decisioning (approve/deny, score, allocate), generation (draft, summarise), classification (label, route, triage), recommendation (rank, suggest), automation (multi-step actions), forecasting (predict outcomes). | |
| impact | Yes | Who is affected: internal (employee-only), b2b (business customers), consumer (end users), regulated (healthcare, finance, public sector). |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Despite no annotations, the description discloses key behavioral traits: deterministic (no LLM call, same inputs produce same outputs). This adds crucial transparency beyond the schema, though it doesn't mention error handling or permission requirements.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Two sentences with no wasted words. The first sentence front-loads the core purpose and key feature (deterministic), and the second provides usage guidance. Highly efficient.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's simplicity (3 enum parameters, no output schema), the description fully covers what an agent needs: purpose, frameworks, determinism, and when to use. No gaps identified.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100% with clear enum descriptions for each parameter. The tool description reinforces the multi-jurisdiction context but doesn't add new semantic detail beyond the schema. Baseline 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's function: '30-second deterministic multi-jurisdiction AI risk classification' across specific frameworks. It distinguishes itself from sibling tool 'classify_annex3' by emphasizing quick orientation over deeper analysis.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
Explicit instruction: 'Use this for quick orientation before deeper analysis.' This tells the agent when to invoke this tool and implies that other tools (like classify_annex3) are for deeper analysis, providing clear usage boundaries.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
TDQS
The two tools have clearly distinct purposes: one is a deep, LLM-powered classification focused on EU AI Act Annex III, while the other is a deterministic, multi-jurisdiction quick risk assessment. There is no overlap or ambiguity.
Both tool names follow a consistent verb_noun pattern using snake_case: classify_annex3 and quick_risk_class. The naming convention is uniform and predictable.
With only 2 tools, the server is minimal but appropriately scoped for a specific domain (AI Act risk classification). Each tool serves a distinct role, so the count is reasonable, though slightly low for broader coverage.
The tools cover deep Annex III classification and quick multi-jurisdiction orientation, but lack other potentially useful operations such as retrieving detailed category information, updating classifications, or generating reports. This creates notable gaps for comprehensive workflows.
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