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Enterprise AI Governance Platform providing trust scoring, bias detection, hallucination detection, guardrails, compliance (NIST AI RMF / EU AI Act / ISO 42001), cost intelligence, drift monitoring, and MCP tool governance via an upstream server registry with trust scoring.
ResponsibleAI provides 7 tools with complete input schemas and descriptions. Naming follows verb_noun convention (rai_*). Descriptions are substantive (100-300+ chars), explaining WHAT the tool does and WHEN to use it. However, parameter descriptions are sparse or missing for several tools (rai_trust_score, rai_cost_estimate lack per-param guidance), and output schemas are not documented. Error handling and recovery guidance are absent. Tool composition is sound, each tool has a single responsibility, but chaining metadata (IDs for downstream calls) is not evident in the schema documentation.
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.
Estimate the USD cost of a model API call from token counts.
Detect hallucination risk in AI-generated text. Analyses hedging language, self-consistency across candidate responses, unsupported factual claims, and (when a `source` is supplied) explicit factual disagreement with that source -- e.g. the source names one day/month/number and the response names another.
Analyze Red Team Responses
Return adversarial attack payloads to probe an AI model for security vulnerabilities. Categories: prompt_injection, jailbreak, data_leakage, role_confusion, delimiter_attack.
rai_redteam_analyze has no visible description or input schema in source. Tool definition is inferred only by name. Per HARD SCORING RULES, this caps the tool at 50 and significantly drags overall score.
Output schemas are not documented for any tool. LLMs cannot plan downstream calls or extract structured data without knowing what fields to expect. Violates pattern:tool and pattern:response-shaper.
rai_trust_score and rai_cost_estimate lack per-parameter descriptions. Parameters like 'fairness', 'privacy', 'model', 'provider' have no guidance on valid ranges, formats, or when to use them. Violates pattern:tool-description.
Inferred effective spec: 2025-06-18+.
| Scored | Grade | Overall | Spec posture | Rubric |
|---|---|---|---|---|
| 2026-09-23 | C | 65 | 2025-06-18+ | v2 |
Scan text for PII (email, phone, SSN, credit card, IP address) and harmful content (hate speech, violence, self-harm). Returns findings and a redacted copy.
Compute a composite AI Trust Score (0-100) across six governance dimensions: fairness, privacy, security, robustness, compliance, authenticity. Returns score, letter grade (A-F), and risk tier (LOW/MEDIUM/HIGH/CRITICAL).
No error handling or recovery guidance documented. Tools do not indicate what errors are retryable, user-fixable, or fatal. Violates pattern:recovery-guide and pattern:error-classification.
rai_redteam_payloads accepts 'categories' array but does not document what happens if an invalid category is passed, or whether the array can be empty. Violates pattern:constrained-input.