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ark-forge

eu-ai-act-scanner

by ark-forge

scan_project

Scan your project to determine EU AI Act obligations. Detects AI/ML frameworks and returns risk category and required legal actions before shipping.

Instructions

Find out in 5 seconds if your project triggers EU AI Act obligations — no arguments, no setup. Scans for 22 AI/ML frameworks (OpenAI, Anthropic, LangChain, HuggingFace, PyTorch, TensorFlow, scikit-learn…), returns your risk category and the legal actions required before you ship. Enforcement live since Feb 2025 — fines up to 35M EUR. For EU AI Act + GDPR together, call combined_compliance_report() instead.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
project_pathNoPath to the project root. Omit entirely or pass '.' to scan the current working directory — no path discovery needed..
follow_importsNoWhen true, also flag files that transitively import AI-flagged modules. Default false is fine for most projects.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.5.0

TDQS

A4.5/5.0
Behavior4/5

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

With no annotations, the description covers key behaviors: it is a fast scan (5 seconds), requires no arguments or setup, scans across 22 frameworks, and returns a risk category plus required legal actions. It does not mention side effects or network usage, but for a read-only scan tool these omissions are minor, so a slight deduction from full marks is appropriate.

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?

The description is concise and front-loaded; the first sentence states the core value proposition, the second explains what it scans and returns, and the third gives a necessary alternative. No unnecessary words 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?

For a tool with zero required parameters and full schema coverage, the description provides a complete picture: purpose, behavior, output, and a clear alternative. The return value is described as 'risk category and the legal actions required', which is sufficient without an output 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?

The input schema already provides descriptions for both parameters (100% coverage). The description adds little beyond saying 'no arguments', which is consistent with both parameters having defaults. Since the schema handles parameter semantics, the baseline of 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 clearly states the tool's function: 'Find out in 5 seconds if your project triggers EU AI Act obligations' and specifies it scans for 22 AI/ML frameworks. It distinguishes itself from siblings by explicitly telling users to call combined_compliance_report instead for EU AI Act + GDPR combined.

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

Usage Guidelines5/5

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

Explicit usage guidance is provided: 'For EU AI Act + GDPR together, call combined_compliance_report() instead.' This directly tells the agent when not to use this tool and offers a clear alternative, satisfying the when/when-not criterion.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.