eu-ai-act-mcp

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 Art. 51 using training FLOPs or Commission designation, and get applicable Art. 53/55 obligations.

Instructions

Determine whether a general-purpose AI model qualifies as a GPAI model with systemic risk under Art. 51. This tool uses an adjudicated conservative boundary at or above 10^25 cumulative training FLOPs for the Art. 51(2) presumption. 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

Name	Required	Description
`model_name`	No	Optional model name for traceability in the response.
`training_flops`	No	Cumulative training compute in FLOPs (e.g. 2e25). The adjudicated product boundary triggers the Art. 51(2) presumption at or above 1e25. Omit if unknown; the tool then abstains instead of answering.
`commission_designated`	No	Whether the Commission has formally designated the model as GPAI with systemic risk under Art. 51(1)(b).

Output Schema

TableJSON Schema

Name	Required	Description	Default
`model_name`	Yes
`flops_threshold`	Yes
`notification_duty`	Yes
`relevant_articles`	Yes
`crosses_flops_threshold`	Yes
`systemic_risk_designation`	Yes
`is_gpai_with_systemic_risk`	Yes
`baseline_obligations_art_53`	Yes
`systemic_risk_obligations_art_55`	Yes

Schema Changelog

Changes observed during successful MCP inspections.

Changed5 schema fields changedv1.5.0Aug 19, 2026

changedInput schema / properties / training_flops / description

Previous value: -"Cumulative training compute in FLOPs (e.g. 2e25). Art. 51(2) presumes systemic risk when > 1e25."New value: +"Cumulative training compute in FLOPs (e.g. 2e25). The adjudicated product boundary triggers the Art. 51(2) presumption at or above 1e25. Omit if unknown; the tool then abstains instead of answering."

addedInput schema / properties / training_flops / minimum
```
Added value: +0
```

changedOutput schema / properties / crosses_flops_threshold / type

Previous value: -"boolean"New value: +[
+  "boolean",
+  "null"
+]

changedOutput schema / properties / is_gpai_with_systemic_risk / type

Previous value: -"boolean"New value: +[
+  "boolean",
+  "null"
+]

changedOutput schema / properties / systemic_risk_designation / enum

Previous value: -[
-  "threshold_met",
-  "commission_designated",
-  "none"
-]New value: +[
+  "threshold_met",
+  "commission_designated",
+  "none",
+  "undetermined"
+]

First observedv1.1.5Jun 14, 2026

TDQS

A4.2/5.0

Behavior4/5

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

Annotations already declare readOnlyHint, idempotentHint and openWorldHint=false, so safety is covered. The description adds meaningful behavior beyond them: the tool abstains when training_flops is omitted rather than guessing, and it returns three distinct obligation sets (Art. 53 baseline, Art. 55 systemic-risk-only, Art. 52 notification duty).

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?

Three sentences, each earning its place: the decision, the threshold rule, and the returned obligations are front-loaded in that order. Slightly dense with statutory citations, but no filler or redundancy.

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?

With an output schema present, the description need not detail return shapes, and it still summarizes the three obligation sets it produces. Combined with the threshold rule, the designation route, and the abstention case, an agent has everything needed to call it correctly.

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% and the schema already documents the 1e25 boundary, the meaning of commission_designated, and the abstention-on-omission behavior. The description's Art. 51(2)/51(1)(b) framing adds legal context but no operational detail the schema lacks, so the baseline 3 applies.

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 names a specific verb ('Determine whether') and a precise legal resource ('GPAI model with systemic risk under Art. 51'), anchoring the tool to a citation an agent can match against its intent. It is clearly distinguishable from siblings like euaiact_assess_system or euaiact_get_obligations.

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

Usage Guidelines4/5

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

It establishes the decision context: the 10^25 cumulative-FLOPs boundary triggers the Art. 51(2) presumption, and Art. 51(1)(b) designation is a separate route. It also implies the abstention case (omit FLOPs if unknown), though it never explicitly contrasts when to prefer this tool over euaiact_get_obligations or euaiact_answer_question.

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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