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mcp-eu-ai-act

by io.github.ark-forge
OverviewDiscussionsAdmin

suggest_risk_category

Describe what your AI system does in plain language — get back your EU AI Act risk tier, the articles that apply, and your first compliance step. Returns matched category (minimal/limited/high/unacceptable) with confidence level and triggering risk indicators. No project scan needed, no API key.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "system_description": "<system_description>"
    },
    "name": "suggest_risk_category"
  }
}

Input Schema

NameRequiredDescriptionDefault
system_descriptionYesShort description of what the AI system does, e.g. 'chatbot for customer support' or 'CV screening tool for recruitment'.

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed1 schema field changed
    • addedInput schema / properties / system_description / description
      Added value: +"Short description of what the AI system does, e.g. 'chatbot for customer support' or 'CV screening tool for recruitment'."
  2. First observed

TDQS

A4.2/5.0
Behavior4/5

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

Since no annotations are provided, the description carries the full burden of behavioral disclosure. It discloses the output (category, confidence, indicators, articles, first step) and key constraints (no project scan, no API key). It does not mention potential caveats like the assessment being preliminary or non-authoritative, but it gives a clear picture of what to expect.

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: it immediately tells the user what to do and what to expect. Every sentence adds value (input, output, constraints) without repetition or filler.

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?

For a one-parameter tool with no output schema, the description adequately covers input requirements and output contents, including the specific fields returned (category, confidence, indicators). It lacks details on the format of articles and compliance steps, but these are secondary to the core purpose.

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 schema already covers 100% of the parameter with a clear description and example. The tool description reinforces 'plain language' but adds no new meaning beyond the schema, so 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: it takes a plain-language description of an AI system and returns the EU AI Act risk tier, applicable articles, and first compliance step. It further specifies the returned data (matched category, confidence level, triggering indicators) and distinguishes itself from siblings with 'No project scan needed, no API key.'

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?

The description implies when to use this tool: when you have a system description and need a quick risk classification. It contrasts with project-scanning siblings by noting no scan or API key is required, which provides useful context. However, it does not explicitly state alternative tools for other use cases or provide explicit 'when not to use' 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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