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NikitaDatar

Vector Decisions MCP Server

by NikitaDatar

compliance_check

Evaluate AI system compliance with EU AI Act by assessing risk category, human oversight, documentation, and governance measures to identify regulatory gaps.

Instructions

EU AI Act compliance check. Evaluates an AI system against EU AI Act requirements.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
categoryYesEU AI Act risk category
systemNameYesName of the AI system
dataGovernanceYesWhether data governance measures are implemented
hasTransparencyYesWhether transparency obligations are met
hasDocumentationYesWhether technical documentation exists
hasHumanOversightYesWhether human oversight is implemented
hasRiskManagementYesWhether risk management system is in place
hasAccuracyMetricsYesWhether accuracy and robustness metrics are tracked
Behavior2/5

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It only says 'evaluates,' which implies a read-only check, but does not disclose whether the operation is reversible, has side effects, requires specific permissions, or what it returns. For a compliance evaluation with 8 required parameters, this is insufficient.

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?

The description is a single concise sentence with no redundant wording. It front-loads the core purpose. However, it sacrifices essential behavioral and usage details for brevity, so it is efficient but under-specified.

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?

For a tool with 8 required parameters and no output schema or annotations, the description is far too sparse. It does not explain how compliance is determined, what the output looks like, how the risk categories are used, or what the boolean flags imply. An agent cannot confidently call this tool correctly based on the description alone.

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 all parameters are already documented in the schema. The description adds no additional parameter-level meaning or context. Per the rubric, a baseline of 3 is appropriate when the schema fully covers the parameters.

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?

The description states a specific verb and resource: 'evaluates an AI system against EU AI Act requirements.' This is clear and distinct from siblings like risk_assessment, though it does not explicitly name alternatives. The tool's purpose is unambiguous.

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 guidance is given on when to use this tool versus the sibling tools (e.g., risk_assessment, assure_action). The description does not mention prerequisites, context, or when to choose an alternative. An agent would have to infer usage from the name alone.

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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