whitepact

Name: whitepact
Author: guruprasathannadurai-official

该来源不提供完整文件导出（国内平台多为平台内托管），仅存元数据与原链

接入信息

传输形态: http
鉴权方式: 鉴权未知
端点: https://whitepact--guruprasathannadurai-official.run.tools

鉴权方式未标注，请核对官方文档后再接入——不要直接使用以下片段

{
  "mcpServers": {
    "whitepact": {
      "url": "https://whitepact--guruprasathannadurai-official.run.tools"
    }
  }
}

能力清单

工具	说明
rai_scan	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.
rai_trust_score	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).
rai_compliance	Evaluate AI governance compliance against NIST AI RMF, EU AI Act, or ISO 42001. Returns compliance score, findings per control, and remediation recommendations.
rai_hallucination	Detect hallucination risk in AI-generated text. Analyses hedging language, self-consistency across candidate responses, and unsupported factual claims.
rai_cost_estimate	Estimate the USD cost of a model API call from token counts.
rai_redteam_payloads	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	Analyse model responses to red team attack payloads. Returns a security report with vulnerability findings, severity breakdown, and an overall security score.
rai_compare_models	Compare two AI models across all six trust dimensions. Returns scores for each, delta analysis, and a recommendation on which model is more trustworthy.
rai_audit_summary	Return a governance capability summary including supported tools, frameworks, and available attack vectors. Full audit log access requires the REST endpoint.
rai_health	Check the status and module availability of the ResponsibleAI governance engine.
rai_bias_evaluate	Evaluate demographic bias across six probe dimensions: gender, racial, age, religious, occupational, and cultural. Provide paired response samples for each demographic group. Returns per-probe bias scores (0=no bias, 1=maximum divergence), confidence intervals, intersectional amplification, and an overall bias grade.
rai_drift_check	Detect trust score drift between a baseline evaluation and a current evaluation. Returns drift delta per dimension, overall drift severity (NONE/LOW/MEDIUM/HIGH/CRITICAL), and whether an alert threshold was breached.
rai_passport_generate	Generate a verifiable AI Passport for a model — a tamper-evident governance card containing trust scores, compliance status, bias summary, and a cryptographic verification hash. Used by Procurement/Legal for third-party AI vendor risk assessment.
rai_budget_check	Evaluate current AI spending against monthly budget limits. Returns consumption percentage, alert status, per-team and per-model breakdown, and projected month-end spend. Used by LLMOps Engineers and Finance to prevent budget overruns.
rai_policy_check	Evaluate text or a model response against a governance policy. Checks for: prohibited topics, required disclaimers, output length limits, language restrictions, and custom keyword blocklist. Returns pass/fail per policy rule with remediation guidance.
rai_stream_scan	Scan a list of text chunks (as would arrive from an LLM streaming response) for PII and harmful content. Simulates the StreamingScanner guardrail without a live stream. Returns per-chunk scan results and an aggregated summary with stop recommendation.
rai_benchmark	Evaluate pre-collected model responses against a standard benchmark suite. Suites: truthfulqa (factual accuracy), bbq (bias in questions), hellaswag (reasoning). Call rai_benchmark_prompts first to get the question set, collect responses, then pass them here.
rai_benchmark_prompts	Return the question set for a benchmark suite. Use to collect model responses before calling rai_benchmark. Suites: truthfulqa, bbq, hellaswag.
rai_model_route	Recommend the optimal AI model for a task based on complexity analysis and cost-quality tradeoff. Returns recommended model, alternative, estimated cost per 1K tokens, and estimated savings vs GPT-4o. Used by LLMOps Engineers for intelligent model routing.
rai_pii_report	Generate a detailed PII audit report for a document or corpus. Classifies findings by PII category (email, phone, SSN, credit card, IP, address), counts occurrences, computes a privacy risk score, and provides GDPR/CCPA remediation guidance. Used by Privacy Engineers for compliance evidence collection.
rai_incident_log	Create a structured governance incident record. Used by Security Engineers and AI Risk Analysts to log AI safety events (PII leaks, jailbreak attempts, bias triggers, hallucination incidents) for audit trail and SIEM integration.
rai_eu_ai_act_classify	Classify an AI system into an EU AI Act risk tier: UNACCEPTABLE, HIGH, LIMITED, or MINIMAL. Evaluates deployment context, capabilities, and affected populations against Annex III and Annex VI criteria. Returns risk tier, applicable articles, required conformity assessment actions, and a compliance roadmap. Used by AI Compliance Managers.
rai_iso42001_gap	Perform an ISO/IEC 42001:2023 AI Management System gap analysis. Evaluates maturity across all 10 clauses: Context, Leadership, Planning, Support, Operation, Performance Evaluation, Improvement, plus AI-specific annexes. Returns gap findings, maturity scores per clause, and a prioritised remediation roadmap. Used by AI Compliance Managers.
rai_executive_summary	Generate a board-ready executive AI governance summary. Synthesises trust grades, compliance posture, cost intelligence, risk incidents, and drift trends into a C-suite-readable report with RAG (Red/Amber/Green) status indicators. Used by CAIO for quarterly board reporting.
rai_org_status	Return a structured governance status snapshot for an organisation. Summarises active models, trust grade distribution, compliance coverage, open risks, and MCP tool usage. Used by CAIO and AI Governance Engineers for dashboards.
rai_check_trust	Check the public, independently-verifiable Trust Index score, certification status, and reported-incident history for a named AI model or tool BEFORE invoking it. Unlike every other rai_* tool, which evaluates output the caller itself produced, this one looks up a public record about a THIRD PARTY'S model or tool — built for agents and agent frameworks (LangChain, LangGraph, Google ADK) deciding whether to trust something before calling it. Free, no auth required, exact model+provider match. Queries the hosted ResponsibleAI Trust Index (configurable via the RAI_TRUST_API_BASE environment variable). Returns 'known: false' for anything never assessed — that is not an error, just an absence of data; self-assessment is free at POST /api/trust-index/assess.
rai_webhook_status	Check webhook delivery health and generate a structured status report. Takes delivery statistics and returns health grade, failure analysis, dead-letter queue status, and recommended remediation actions. Used by Security Engineers feeding SIEM systems and Platform Engineers debugging webhook pipelines.

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