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classify_annex3

Classify your product against all 8 EU AI Act Annex III high-risk categories with full regulatory context. Get 5 priority remediation actions tailored to your stack in about 60 seconds.

Instructions

Deep Annex III classification using Claude (Anthropic) with full EU AI Act regulatory context. Maps your product against all 8 Annex III high-risk categories + Article 50 transparency + GPAI Article 53 + GPAI provider/deployer reasoning. Returns 5 priority remediation actions tailored to your stack. ~60 seconds (LLM-powered).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
companyYesCompany name being classified.
industryYesIndustry/vertical (e.g., 'HR-tech', 'fintech', 'healthtech', 'productivity', 'voice-AI').
featuresYesAI features in the product. Examples: 'content-generation', 'scoring', 'decision-making', 'api-consumer', 'own-model', 'recommendation', 'classification'.
useCaseYesPlain-English description of what the AI does and who it serves. Min 20 chars.
euExposureYesEU exposure level. 'eu-customers-output' = serves EU customers with AI-influenced outputs. 'eu-employees-only' = internal tools used by EU employees only. 'no-eu' = no EU footprint. 'considering-eu' = planning EU expansion.

Implementation Reference

  • src/index.ts:247-279 (registration)
    Tool registration of classify_annex3 as an MCP tool, defining its name, description, and inputSchema (company, industry, features, useCase, euExposure).
    {
      name: "classify_annex3",
      description:
        "Deep Annex III classification using Claude (Anthropic) with full EU AI Act regulatory context. Maps your product against all 8 Annex III high-risk categories + Article 50 transparency + GPAI Article 53 + GPAI provider/deployer reasoning. Returns 5 priority remediation actions tailored to your stack. ~60 seconds (LLM-powered).",
      inputSchema: {
        type: "object",
        properties: {
          company: {
            type: "string",
            description: "Company name being classified.",
          },
          industry: {
            type: "string",
            description: "Industry/vertical (e.g., 'HR-tech', 'fintech', 'healthtech', 'productivity', 'voice-AI').",
          },
          features: {
            type: "array",
            items: { type: "string" },
            description: "AI features in the product. Examples: 'content-generation', 'scoring', 'decision-making', 'api-consumer', 'own-model', 'recommendation', 'classification'.",
          },
          useCase: {
            type: "string",
            description: "Plain-English description of what the AI does and who it serves. Min 20 chars.",
          },
          euExposure: {
            type: "string",
            enum: ["eu-customers-output", "eu-employees-only", "no-eu", "considering-eu"],
            description: "EU exposure level. 'eu-customers-output' = serves EU customers with AI-influenced outputs. 'eu-employees-only' = internal tools used by EU employees only. 'no-eu' = no EU footprint. 'considering-eu' = planning EU expansion.",
          },
        },
        required: ["company", "industry", "features", "useCase", "euExposure"],
      },
    },
  • Handler for classify_annex3 tool call. Makes a POST request to https://eucomplyhub.com/api/annex3-classify with the provided arguments, returns the JSON response, or an error message if the API call fails.
    if (name === "classify_annex3") {
      try {
        const response = await fetch(`${API_BASE}/api/annex3-classify`, {
          method: "POST",
          headers: { "Content-Type": "application/json" },
          body: JSON.stringify(args),
        });
    
        if (!response.ok) {
          const errText = await response.text();
          throw new Error(`Classifier API returned ${response.status}: ${errText.slice(0, 200)}`);
        }
    
        const result = await response.json();
        return {
          content: [
            {
              type: "text",
              text: JSON.stringify(result, null, 2),
            },
          ],
        };
      } catch (err: any) {
        return {
          content: [
            {
              type: "text",
              text: `Error calling classifier: ${err.message}. Try the web version: https://eucomplyhub.com/annex3`,
            },
          ],
          isError: true,
        };
      }
    }
  • File header describing classify_annex3 as a 'Deep Annex III classification (Claude-powered)' tool that maps to https://eucomplyhub.com/api/annex3-classify.
    #!/usr/bin/env node
    
    /**
     * @eucomplyhub/mcp-eu-ai-act
     *
     * MCP (Model Context Protocol) server exposing eucomplyhub.com's free EU AI Act
     * compliance classifiers to AI assistants (Claude Desktop, Cursor, Windsurf, etc.)
     *
     * Two tools exposed:
     *   1. classify_annex3 — Deep Annex III classification (Claude-powered)
     *      Maps to: https://eucomplyhub.com/api/annex3-classify
     *   2. quick_risk_class — 30-second multi-jurisdiction risk classifier
     *      (deterministic, no LLM call — same logic as eucomplyhub.com/risk-class)
     *
     * @license MIT
     * @see https://eucomplyhub.com
     */
Behavior3/5

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

No annotations exist, so the description carries the full burden. It discloses that the tool is 'LLM-powered' and takes ~60 seconds, but does not mention side effects, authentication needs, or data handling beyond the input schema. It provides moderate transparency.

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 (three sentences) with no wasted words. It front-loads the purpose, then adds detail, and ends with timing. Every sentence adds value.

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 classification, the description covers the scope (categories, articles), input (via schema), and output (5 remediation actions). No output schema, but it specifies what is returned. Could mention output format explicitly.

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%—every parameter has a description. The tool description does not add additional meaning beyond the schema for individual parameters. Baseline 3 is appropriate.

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 it performs 'Deep Annex III classification' using Claude, mapping against specific regulatory categories (8 Annex III, Article 50, GPAI), and returns remediation actions. This distinguishes it from the sibling 'quick_risk_class' which is likely a lighter alternative.

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 it is a deep, time-consuming (~60 seconds) tool but does not explicitly state when to use it over 'quick_risk_class' or when not to use it. Usage guidance is implicit rather than explicit.

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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