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classify_system

Classifies the EU AI Act risk level for an AI system based on its type and use case, specifying which compliance obligations apply.

Instructions

Classify the EU AI Act risk level for a specific AI system based on its type and use case. Helps determine which obligations apply.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
use_caseYesDescription of how the system is used
use_domainNoThe domain where the AI system is deployedother
system_nameYesName of the AI system (e.g., 'OpenAI GPT-4', 'Custom PyTorch model')
processes_personal_dataNoWhether the system processes personal data
makes_decisions_about_peopleNoWhether outputs affect decisions about individuals
Behavior2/5

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

No annotations are provided, so the description bears full responsibility. It only says 'Classify' without disclosing behavioral traits such as whether it makes external API calls, has side effects, or requires specific authorization. The description does not add context beyond the tool's one-word action.

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

Conciseness3/5

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

The description is a single sentence, which is concise but lacks structure. It could be improved by front-loading key details or using bullet points. However, it is not verbose.

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

Completeness2/5

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

No output schema exists, so the description should hint at return values. It mentions determining which obligations apply but does not specify the classification categories (e.g., minimal, limited, high, unacceptable) or any other output details. The description is incomplete for a classification tool.

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 input schema already documents all parameters. The description mentions 'type and use case', which loosely maps to 'system_name' and 'use_case', but adds no further semantic value beyond what the schema provides.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

Description uses a specific verb 'Classify' and identifies the resource as 'EU AI Act risk level', which clearly conveys the tool's function. While it distinguishes from siblings like 'quick_check' and 'scan_project', it does not explicitly differentiate them.

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

Usage Guidelines2/5

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

No explicit guidance on when to use this tool versus alternatives, nor any exclusions or prerequisites. The description implies it should be used to classify AI systems, but it does not state when not to use it or mention sibling tools.

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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