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MCP server providing tools and resources for EU AI Act Article 50 compliance, including transparency obligations for AI systems (AI interaction disclosures, deepfake labeling, watermarking, and risk classification).
This server provides 14 tools for EU AI Act Article 50 compliance. Most tools have clear naming (verb-first: get_, label_, check_, classify_, determine_, list_) and good descriptions (100-300 chars, well above the 10-char minimum). However, schema completeness is inconsistent: tools like get_ai_interaction_disclosure and get_emotion_recognition_disclosure have explicit input parameter definitions with types and descriptions, but several tools (e.g., determine_eu_ai_act_role with 11 required parameters) lack visible constraint definitions (enums, min/max ranges). Output schemas are documented in prose but not formally specified in JSON Schema format. Error handling is minimal, most tools return basic {'error': ...} structures without actionable recovery guidance. The server uses fastmcp framework (current as of 2026-07-28) and registers tools correctly, but lacks tool annotations (readOnlyHint, destructiveHint) and per-request metadata handling. Parameter defaults are generally safe, but some tools accept free-form strings (e.g., language, style) where enums would be better. Overall, the server demonstrates solid fundamentals but falls short of production-grade polish in schema rigor, error guidance, and output consistency.
Check which Article 50 transparency requirements apply to your AI system. Determines applicable obligations based on AI system type and capabilities.
Check if an AI system uses prohibited practices under EU AI Act Article 5. Identifies high-risk prohibited uses including social scoring, emotion recognition in certain contexts, and manipulative profiling.
Determine AI system risk level per EU AI Act classification framework. Classifies system as PROHIBITED, HIGH-RISK, LIMITED-RISK, or MINIMAL-RISK based on Articles 5, 6, and 50.
Determine which EU AI Act role(s) apply to your organization. Different roles have different obligations. Understanding your role is CRITICAL to knowing which requirements apply.
Get AI interaction disclosure text for EU AI Act Article 50(1) compliance. This tool provides pre-written disclosure text that MUST be shown to users when they interact with an AI system (chatbots, voice assistants, etc.).
Parameters accept free-form strings where enums would enforce valid values. Example: 'language' accepts 'en', 'es', 'fr', 'de', 'it' but no enum constraint prevents LLMs from passing invalid codes like 'zh' or 'jp'. This violates constrained-input pattern.
Output schemas are documented in docstrings (e.g., 'Returns: Dictionary containing...') but not formally specified as JSON Schema. LLMs cannot machine-parse expected fields, forcing them to infer structure from the prose description. This creates ambiguity and wastes tokens.
| Scored | Grade | Overall | Spec posture | Rubric |
|---|---|---|---|---|
| 2026-09-22 | D | 58 | 2026-07-28+ | v2 |
| 2026-03-09 | F | 36 | - | v1 |
Get all available deepfake and AI-generated content labels. This tool returns the complete set of labels available for different content types. Use this to see what labels are available for images, videos, audio, and text.
Get emotion recognition disclosure text for EU AI Act Article 50(3) compliance. This tool provides pre-written disclosure text that MUST be shown to users when an AI system uses emotion recognition technology.
Get watermarking configuration and technical standards for Article 50(2). Provides C2PA specifications, IPTC metadata standards, and implementation guidance for machine-readable watermarks on AI-generated content.
Generate deepfake label for AI-generated or manipulated audio per Article 50(4). Provides metadata, insertion methods, and technical requirements for labeling synthetic audio.
Generate deepfake label for AI-generated or manipulated content per Article 50(4). This consolidated tool handles labeling for all content types: text, image, video, and audio.
Generate deepfake label for AI-generated images per Article 50(4). Provides label text, placement guidance, and implementation requirements for making AI-generated images visually identifiable.
Add AI-generated content disclosure to news articles and public interest text. This tool implements EU AI Act Article 50(4) compliance for AI-generated text published as news, journalism, or public interest content.
Generate deepfake label for AI-generated or manipulated videos per Article 50(4). Provides label text, placement guidance, and technical implementation requirements for video content.
List all loaded plugins and their capabilities. Returns information about each plugin including name, description, tools provided, resources provided, and enabled status.
Error handling is minimal. Tools return {'error': ...} but do not guide the LLM on recovery. Example: get_ai_interaction_disclosure returns a basic error when language/style is not found, but does not suggest what the LLM should try next (e.g., 'Try one of these languages: en, es, fr, de, it').
Tool determine_eu_ai_act_role has 11 boolean parameters (develops_ai_system, uses_ai_system, sells_ai_system, etc.) but no indication of mutual exclusivity, required combinations, or validation. An LLM passing all as true or all as false will succeed with ambiguous results. Parameter relationships are undocumented.
No tool annotations (readOnlyHint, destructiveHint, idempotentHint) present. All 14 tools appear to be read-only based on risk classification, but this is not explicitly declared in the tool definition. Agents cannot distinguish safe read-only tools from potential write operations without reading descriptions.
Tools like classify_ai_system_risk and check_prohibited_practices lack concrete examples of input and output. The description states what they do but does not show sample calls or return structures. This forces LLMs to guess parameter values and expected response fields.
Parameter 'style' in disclosure tools (get_ai_interaction_disclosure, get_emotion_recognition_disclosure) accepts values like 'simple', 'detailed', 'voice', 'privacy_notice' but these are documented as free-form strings, not enums. No validation prevents typos like 'simplify' or 'detailed_voice'.
list_plugins has no input parameters documented. The description says 'Input: {}' but does not explain what the tool does if plugins are not loaded, or how to filter/search plugins. No output schema. Agents cannot plan calls that depend on plugin availability.