WhitePact

Evaluate Governance Compliance

rai_compliance

Read-onlyIdempotent

Evaluate AI governance maturity against NIST, EU AI Act, or ISO 42001 using self-assessed control scores. Get compliance scores, findings, and remediation steps.

Instructions

Evaluate AI governance maturity against NIST AI RMF, EU AI Act, or ISO 42001, given your own self-assessed control scores (fairness/privacy/security/robustness 0-1, plus overall compliance maturity 0-1). Returns a compliance score, findings per control, and remediation recommendations. Use this when the caller already has maturity/control scores and wants a gap assessment. Do NOT use this to classify what EU AI Act risk tier a specific system falls into from a description of what it does (sector, automation, biometric use, etc.) -- use rai_eu_ai_act_classify instead for that; this tool has no equivalent inputs (no deployment sector, no system description) and cannot answer that question.

Tool call template

This tool declares no required parameters. Add optional parameters from the input schema as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {},
    "name": "rai_compliance"
  }
}

Input Schema

Name	Required	Default
`use_case`	No	general
`framework`	No	NIST_AI_RMF
`privacy_score`	No
`fairness_score`	No
`security_score`	No
`robustness_score`	No
`compliance_maturity`	No

Schema Changelog

Changes observed during successful MCP inspections.

First observedv1.2.6Sep 11, 2026

TDQS

A4.3/5.0

Behavior4/5

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

Annotations already declare readOnly/idempotent/non-destructive, so the safety profile is covered. The description adds genuinely new behavioral context beyond them: it names the return payload (compliance score, per-control findings, remediation recommendations) and the precondition that the caller must supply self-assessed scores. No output schema exists, so this disclosure is valuable. It stops short of mentioning scoring determinism or how defaults (all 0.5) are treated.

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?

Front-loaded with the core purpose and return values, then the routing rule and exclusion. The negative-guidance sentence is long, but every clause earns its place by fending off a specific misrouting. Slightly verbose overall but no 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 read-only scoring tool with no output schema, the description supplies the returns, the input precondition, and the anti-pattern that would cause misuse. The remaining gap is the undocumented 'use_case' parameter and default behavior, which is the only thing an agent could still get wrong.

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 0% across 7 parameters, so the description must carry the burden. It does explain the five numeric control scores and their 0-1 range, and implies the framework values, but leaves 'use_case' entirely undefined and does not clarify that all scores default to 0.5 or what the framework choice changes in the output. Partial compensation only.

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?

States a specific verb and resource ('Evaluate AI governance maturity') and names the exact frameworks it scores against (NIST AI RMF, EU AI Act, ISO 42001). It explicitly differentiates itself from the closest sibling, rai_eu_ai_act_classify, so an agent can route without opening either schema.

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?

Provides both the positive trigger ('when the caller already has maturity/control scores and wants a gap assessment') and an explicit exclusion with the alternative named ('Do NOT use this to classify what EU AI Act risk tier... use rai_eu_ai_act_classify instead'). It even explains why the alternative is needed (no deployment sector, no system description inputs).

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