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Calculate EU AI Act Penalties

euaiact_calculate_penalty
Read-onlyIdempotent

Calculate maximum fine for EU AI Act violations based on violation type, annual turnover, and SME status. Implements Art. 99 penalty framework with SME reduction.

Instructions

Calculates the maximum possible fine for an EU AI Act violation based on violation type, global annual turnover, and SME status. Implements the Art. 99 penalty framework including the SME/startup protection rule (Art. 99(6)). Returns a comparative block so the agent can show the SME reduction to the user.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
violation_typeYesType of AI Act violation: 'prohibited' (Art. 5), 'high_risk' (Art. 99(4) operator/notified-body/transparency obligations), 'gpai' (Art. 101 general-purpose AI model provider infringements), or 'false_info' (Art. 99(5) misleading notified bodies or national competent authorities; GPAI-provider false information falls under Art. 101)
annual_turnover_eurYesGlobal annual turnover in EUR
is_smeNoWhether the entity is an SME or startup (eligible for lower fines under Art. 99(6))

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
violation_typeYes
is_smeYes
annual_turnover_eurYes
max_fineYes
tier_detailsYes
comparativeNo
Behavior4/5

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

Annotations declare readOnlyHint and idempotentHint, so agent knows no side effects. Description adds the key behavioral detail: it returns a comparative block for SME reduction display, which is valuable beyond annotations.

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

Conciseness5/5

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

Two sentences, front-loaded with action and inputs, no redundant info. Every sentence adds value.

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

Completeness5/5

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

For a calculator tool with output schema, the description covers all needed functional aspects: what it calculates, the legal basis, and output format. No gaps given the richness of annotations and schema.

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 coverage is 100% with good descriptions. The description adds legal context (Art. 99) and mention of SME reduction but does not significantly augment parameter meaning beyond 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?

Description uses specific verb 'calculates' and resource 'penalty' with clear inputs (violation type, turnover, SME status). Distinguishes from sibling tools like euaiact_classify_system by focusing on penalty calculation.

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?

Implicitly clear when to use (penalty calculation), but no explicit when-not-to-use or comparison with alternatives. Lacks explicit guidance for agent to choose this over siblings.

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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