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CSOAI-ORG

EU AI Act Compliance MCP

audit_report

Generate a complete EU AI Act audit report by classifying the AI system, checking compliance, generating documentation, and assessing penalties.

Instructions

Generate a complete EU AI Act audit report.

Runs classification, compliance check, documentation generation, and penalty assessment — then combines everything into a comprehensive markdown audit report. This is the all-in-one tool for compliance officers.

Args: system_name: Name of the AI system. provider_name: Legal name of the AI system provider. provider_contact: Provider contact details. version: System version number. purpose: System's intended purpose and use context. description: General description of the system. data_types: Types of data processed. decision_scope: What decisions the system makes or assists with. architecture_description: Description of system architecture. has_risk_management: Whether risk management system exists. has_data_governance: Whether data governance practices exist. has_technical_docs: Whether technical documentation exists. has_logging: Whether automatic logging is implemented. has_transparency_info: Whether transparency info exists. has_human_oversight: Whether human oversight measures exist. has_accuracy_testing: Whether accuracy/robustness testing is done. annual_global_turnover_eur: Annual global turnover in EUR. is_sme: Whether the company is an SME. caller: Identifier for rate limiting. tier: "free" (10 calls/day) or "pro" (unlimited, $29/mo).

Behavior: This tool generates structured output without modifying external systems. Output is deterministic for identical inputs. No side effects. Free tier: 10/day rate limit. Pro tier: unlimited. No authentication required for basic usage.

When to use: Use this tool when you need to assess, audit, or verify compliance requirements. Ideal for gap analysis, readiness checks, and generating compliance documentation.

When NOT to use: Do not use as a substitute for qualified legal counsel. This tool provides technical compliance guidance, not legal advice.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
system_nameYes
provider_nameYes
provider_contactYes
versionYes
purposeYes
descriptionYes
data_typesYes
decision_scopeYes
architecture_descriptionYes
has_risk_managementNo
has_data_governanceNo
has_technical_docsNo
has_loggingNo
has_transparency_infoNo
has_human_oversightNo
has_accuracy_testingNo
annual_global_turnover_eurNo
is_smeNo
callerNoanonymous
tierNofree
api_keyNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior4/5

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

With no annotations, the description takes responsibility. It clearly states no side effects, deterministic output, rate limits (free/pro tiers), and no authentication needed. Could add error handling details, but the given info is solid.

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?

Well-organized with sections for Args, Behavior, and usage. Front-loaded with the main purpose. The parameter list is long but necessary; each entry is concise. The structure aids readability.

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

Completeness3/5

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

Description covers purpose, parameters, behavior, and usage. However, it omits details about the output structure beyond 'comprehensive markdown audit report', despite having an output schema. Also lacks explicit cross-references to sibling tools for more specific tasks.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0%, so the description must compensate. It provides a label for each of the 21 parameters (e.g., 'system_name: Name of the AI system'), adding meaning beyond the schema titles. This is thorough, though a few descriptions are repetitive.

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 generates a complete EU AI Act audit report, listing specific components (classification, compliance check, documentation, penalty assessment). It distinguishes itself from siblings by being 'all-in-one', while siblings like classify_ai_risk and check_compliance are more granular.

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 'When to use' and 'When NOT to use' sections provide clear guidance: use for compliance audits, not as legal advice. This helps the agent choose appropriately and sets expectations about limitations.

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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