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ClevSec EU compliance tools

EU AI Act scope and risk classification check

ai_act_scope_check
Read-onlyIdempotent

Use when someone asks whether the EU AI Act applies to an AI system or model, how it is classified or what they must do ('is our recruitment tool high-risk?', 'does the AI Act apply to our chatbot?', 'what are our AI Act obligations as a deployer?'). Call without answers to get the questions. Call with answers about one system to get whether it is in scope, the role (provider, deployer, importer, distributor; Art. 25 role change), the classification (prohibited, high-risk under Annex I or III, Art. 6(3) exception, Art. 50 transparency, minimal risk, GPAI model), and each obligation with its article and the date it applies from, using the dates of the AI Act as amended by the 2026 Digital Omnibus (Regulation (EU) 2026/1744). A self-assessment from the user's answers, not legal advice.

Tool call template

This tool declares no required parameters. Add optional parameters from the input schema as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {},
    "name": "ai_act_scope_check"
  }
}

Input Schema

NameRequiredDescriptionDefault
answersNoAnswers about one AI system; call without answers for the question texts. Lists: prohibited (p1-p9), annex_iii (h1-h8), transparency (t1-t5). Omitted lists count as none, omitted yes/no answers as no.

Schema Changelog

Changes observed during successful MCP inspections.

  1. Added

TDQS

A4.3/5.0
Behavior4/5

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

Annotations already establish a safe, idempotent, closed-world read, so the description focuses on what the agent actually receives: in-scope status, role including Art. 25 role change, classification across prohibited/Annex I/Annex III/Art. 6(3)/Art. 50/minimal/GPAI, and each obligation with its article and applicability date. It also discloses a substantive behavioral caveat — the output is a self-assessment, not legal advice — and pins the legal baseline to the Act as amended by the 2026 Digital Omnibus. What it does not disclose is any limitation of the ruleset's coverage (e.g., jurisdiction or currency beyond that single reference), which keeps it short of a 5.

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?

The opening sentence is front-loaded with the trigger and the parenthetical examples make the intent instantly recognizable. The second and third sentences cover the two modes and the full return payload efficiently. It is dense and long for a single paragraph, but every clause carries information an agent needs.

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?

With no output schema, the description carries the return-value burden and does so well, enumerating the determination components (scope, role, classification, obligations with articles and dates). For a single-parameter tool wrapping a 13-field nested answers object, this is nearly complete; the remaining gap is that the enum codes for prohibited, annex_iii and transparency lists are never decoded, leaving the most complex part of the input opaque.

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 the schema already carries the default semantics ('omitted lists count as none, omitted yes/no answers as no') and the two-mode behavior is restated in the description. The description adds little beyond that: the enumerated codes p1–p9, h1–h8 and t1–t5 are named as lists but never given meanings, so an agent cannot interpret an answer value from the definition alone. Baseline 3 is appropriate where the schema does the heavy lifting.

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?

The description states a specific resource and operation — determining whether the EU AI Act applies to an AI system and how it is classified — and immediately supports it with concrete user phrasings ('is our recruitment tool high-risk?'). It is clearly separable from sibling scope checks such as nis2_scope_check or agent_risk_check because the subject matter (AI Act, risk tiers, GPAI) is named explicitly.

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

Usage Guidelines5/5

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

It gives an explicit trigger condition ('Use when someone asks whether the EU AI Act applies...') plus three example questions that map directly onto the classification and obligation outputs. It also states the two invocation modes — call without answers to get the questionnaire, call with answers about one system to get the determination — which removes ambiguity about how to start.

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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