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ai-consent

by Yabloko Labs Ltd.OfficialGitHubWebsiteUpdated Aug 23, 2026

Ensure your AI agents meet EU AI Act requirements with automated risk classification and compliance mapping. Developers can scan deployments to generate readiness scores, identify regulatory gaps, and receive prioritized remediation steps to achieve full technical conformity.

How to pay

Subscribe

Monthly billing

$5/month

Predictable monthly cost with included usage. Best for steady, high-volume traffic.

  • Unlimited tools within plan limits
  • One API key, billed once a month
  • Cancel any time

Overview

The ai-consent MCP server provides a standardized framework for auditing AI agents against the EU AI Act regulatory requirements. It automates the risk classification process and generates actionable remediation roadmaps to ensure your deployments meet legal transparency and safety standards.

Key Capabilities

  • classify_risk: Analyzes agent architecture and data usage to assign an EU AI Act risk level (minimal, limited, high, or unacceptable).
  • audit_compliance: Performs a deep scan of system prompts, training data lineage, and output logging to identify specific regulatory gaps.
  • generate_remediation: Produces a prioritized list of technical fixes and policy updates required to achieve full compliance.
  • verify_alignment: Re-scans updated agent configurations to validate that implemented changes meet the required safety thresholds.

Use Cases

  • Assessing whether a customer-facing chatbot requires mandatory human oversight or specific technical documentation under high-risk classification criteria.
  • Automatically generating a compliance report for internal legal teams by scanning agent logs for prohibited data processing patterns.
  • Iteratively refining an agent’s guardrails by running verify_alignment after applying remediation steps suggested by the server.

Who This Is For

This server is designed for AI engineers, compliance officers, and system architects working within the European market. It is best suited for technical professionals tasked with maintaining regulatory rigor in production-grade machine learning environments.