eu-ai-act

art50_transparency_check

Tjek hvilke EU AI Act Artikel 50-transparenskrav der rammer jeres AI-flader: chatbot/AI-assistent, AI-genereret indhold (tekst/billede/lyd/video), deepfake-risiko og emotion/biometri. Output: konkrete krav, frister (2026-08-02; Art 50(2)-watermarking-grace 2026-12-02 kun for legacy-systemer), tjekliste og kildelinks. Deterministisk, ingen LLM. (EU AI Act Article 50 transparency check, Danish output.)

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "chatbot": "<chatbot>"
    },
    "name": "art50_transparency_check"
  }
}

Input Schema

Name	Required	Description
`chatbot`	Yes	Har I en chatbot/AI-assistent, der interagerer med brugere?
`deepfake_risk`	No	Genererer/manipulerer I medieindhold, der kan fremstå ægte (deepfakes)?
`ai_generated_content`	No	Typer af AI-genereret indhold I udgiver (tom hvis ingen)
`emotion_or_biometric`	No	Kører I følelsesgenkendelse eller biometrisk kategorisering?

Schema Changelog

Changes observed during successful MCP inspections.

First observedOct 20, 2026

TDQS

A3.9/5.0

Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations the description carries the full burden, and it delivers strong behavioural context: deterministic, no LLM, plus exactly what the output contains (konkrete krav, frister, tjekliste, kildelinks). It stops short of stating side effects or permission needs, but that is minor for a stateless checker.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Two front-loaded sentences with the scope first, then output, then the deterministic/no-LLM caveat. Dense but every clause earns its place; the parenthetical language note is a small tail that could be trimmed.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

No output schema exists, so the description correctly summarises the return (requirements, deadlines with specific dates, checklist, source links) and flags that output is Danish. Inputs are fully covered by the schema. Only the exact output format (structured vs prose) remains unspecified.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, so all four parameters are already documented in Danish, and the description merely mirrors the same category list. Baseline 3 applies since the schema does the heavy lifting and the description adds no extra syntax or semantics.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

States a precise verb+resource: a check of which EU AI Act Article 50 transparency requirements apply, and enumerates the four covered surfaces (chatbot/AI-assistent, AI-genereret indhold, deepfake-risiko, emotion/biometri). The Article 50 scoping makes it clearly distinguishable from ai_act_scope_check and eu_ai_act_facts even without naming them.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Usage is implied by the listed input surfaces (if you have a chatbot, run this), but there is no explicit when-to-use statement, no when-not, and no routing to sibling tools such as ai_act_scope_check for broader scope questions. Adequate but leaves the agent to infer selection.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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