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Estimate the maximum EU AI Act fine

estimate_ai_act_fine
Read-only

Maximum administrative fine under Articles 99 and 101 of the EU AI Act for a violation type, given worldwide annual turnover, including the lower-of rule for SMEs (Art. 99(6)).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
isSmeYesThe company is an SME or start-up.
violationYesprohibited: Deploying a system that falls under Article 5. obligations: Failing provider, deployer, importer, distributor or transparency duties. information: Supplying incorrect, incomplete or misleading information to notified bodies or authorities. gpai: Fines imposed by the European Commission on providers of general-purpose AI models.
annualTurnoverEurYesTotal worldwide annual turnover in euros.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4.1/5.0
Behavior4/5

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

Annotations already declare readOnlyHint=true and openWorldHint=false, so safety is covered. The description adds substantive domain context beyond that: it is a maximum (not exact) fine capped by Articles 99/101 and subject to the Art. 99(6) lower-of rule for SMEs, which is real behavioral meaning.

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?

A single dense sentence with no filler, front-loaded with the key concept (maximum administrative fine). Slightly compressed in packing the article references and the SME rule together, but every clause earns its place.

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 does name the returned value (maximum administrative fine) and its legal basis, and covers the three required parameters implicitly. Minor gaps remain (e.g., implied currency, that the result is a static/legal estimate rather than a live query), but nothing blocking.

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

Parameters4/5

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

Schema coverage is 100%, so baseline is 3, but the description adds meaning to isSme by explaining the Art. 99(6) lower-of rule it triggers, and frames annualTurnoverEur as 'worldwide annual turnover' consistent with the schema. This is a modest but genuine addition over the schema.

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 specific verb (Estimate) and resource (maximum EU AI Act fine) with the legal basis (Art. 99/101) and the exact inputs that drive it (violation type, worldwide annual turnover). It is clearly distinguishable from classification, disclosure, or deadline siblings.

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?

The description implies the calculation context (violation type + turnover, SME lower-of rule) but gives no explicit when-to-use guidance, prerequisites, or exclusions versus the sibling tools. Usage is inferable but not stated.

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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