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Check GPAI Model Systemic Risk Classification

euaiact_check_gpai_systemic_risk
Read-onlyIdempotent

Check if a general-purpose AI model qualifies as systemic risk under EU AI Act Art. 51 using training compute thresholds or Commission designation, and retrieve applicable obligations.

Instructions

Determine whether a general-purpose AI model qualifies as a GPAI model with systemic risk under Art. 51. A model is presumed to have high-impact capabilities when cumulative training compute exceeds 10^25 FLOPs (Art. 51(2)). The Commission may also designate models with equivalent capabilities or impact under Art. 51(1)(b). Returns baseline GPAI obligations under Art. 53 plus systemic-risk-only obligations under Art. 55, and the Art. 52 notification duty.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
training_flopsNoCumulative training compute in FLOPs (e.g. 2e25). Art. 51(2) presumes systemic risk when > 1e25.
commission_designatedNoWhether the Commission has formally designated the model as GPAI with systemic risk under Art. 51(1)(b).
model_nameNoOptional model name for traceability in the response.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
model_nameYes
crosses_flops_thresholdYes
flops_thresholdYes
systemic_risk_designationYes
is_gpai_with_systemic_riskYes
baseline_obligations_art_53Yes
systemic_risk_obligations_art_55Yes
notification_dutyYes
relevant_articlesYes
Behavior4/5

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

Annotations already indicate readOnlyHint=true and idempotentHint=true. The description adds value by specifying that the tool returns baseline GPAI obligations under Art. 53, systemic-risk obligations under Art. 55, and notification duty under Art. 52, which is beyond what annotations provide.

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 description is a single paragraph that efficiently conveys the purpose, criteria, and return values. It is neither too long nor too short, though it could be slightly more structured with bullet points for readability.

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?

Given the complexity of EU AI Act regulations, the description covers the critical classification criteria and return obligations. The presence of an output schema (not shown but noted) reduces the burden on the description for explaining return values. Adequate for an agent to understand the tool's role.

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% with good descriptions. The tool description adds context about the legal basis and threshold (Art. 51(2) presumption at >1e25 FLOPs), enhancing the meaning of the training_flops parameter. It also explains the commission_designated parameter's significance under Art. 51(1)(b).

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 clearly states the tool determines if a GPAI model qualifies for systemic risk under Art. 51, including the FLOP threshold and Commission designation. It distinguishes from siblings by focusing on this specific classification task.

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 use when checking systemic risk, but does not explicitly state when not to use this tool or compare with siblings like euaiact_classify_system or euaiact_get_obligations. The context is clear but lacks exclusion guidance.

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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