Stratalize Governance
AI governance intelligence: EU AI Act, FCA PS7/24, NIST AI RMF, OCC enforcement, and state AI laws.
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AI governance intelligence: EU AI Act, FCA PS7/24, NIST AI RMF, OCC enforcement, and state AI laws.
Server tool list (18)
Raw names from tools/list. Only developers need these.
| get_stratalize_overview | START HERE — Returns the complete Stratalize tool catalog: governed MCP tools across finance, healthcare, governance, real estate, crypto, and intelligence. Available via public MCP (no auth) or x402 micropayments on Base ($0.02 atomic · $0.10 benchmark · $0.50 synthesis · $1.00 premium · $3.00 outcome pack). Org intelligence, agent governance, and role briefs require OAuth. Call this first to discover tools by role or vertical. |
| get_adoption_stage | Public mode returns FS AI RMF framework reference data only — not org-specific scoring. Use when assessing an organization FS AI RMF governance maturity stage or preparing a regulatory AI roadmap presentation. Returns INITIAL, MINIMAL, EVOLVING, or EMBEDDED classification with stage criteria and remediation priorities. Example: EVOLVING stage organizations have documented AI policies but lack systematic model validation — typical gap to EMBEDDED is 18-24 months and 12-15 additional controls. Connect org MCP for org-specific scoring. Source: FS AI Risk Management Framework. |
| get_ofac_sanctions_screening | Use when onboarding a vendor, counterparty, or individual requires OFAC sanctions screening with attested results. Screens Treasury SDN and Consolidated lists with conservative fuzzy matching, match methodology metadata, and list version binding for GSR verification. Source: Treasury OFAC synced lists. Cryptographically attested with a post-quantum signed settlement receipt. Verify at trust.stratalize.com/verify. |
| get_oig_exclusion_screening | Use when credentialing a healthcare provider or verifying vendor eligibility against federal exclusions. Screens HHS OIG LEIE by provider name or NPI with list version and as-of date in the attested payload. Source: HHS OIG LEIE synced exclusions. Cryptographically attested with a post-quantum signed settlement receipt. Verify at trust.stratalize.com/verify. |
| get_sam_exclusion_screening | Use when verifying federal contractor or grantee eligibility against SAM.gov exclusions. Queries SAM Exclusions API by entity name, UEI, or CAGE with conservative name matching and live attestation metadata. Source: SAM.gov Exclusions API. Cryptographically attested with a post-quantum signed settlement receipt. Verify at trust.stratalize.com/verify. |
| get_eu_ai_act_coverage | Use when assessing EU AI Act compliance readiness ahead of the August 2, 2026 enforcement deadline or preparing a board AI governance briefing. Returns a composite payload with framework, deadline, total_controls, controls[], hint, and query timestamp, optionally filtered by NIST function from compliance_controls reference data. Example: Filter by MAP to review mapped EU AI Act controls and implementation statuses in the returned controls array for governance planning. Source: EU AI Act mappings in compliance_controls reference data. |
| get_uk_fca_coverage | Use when assessing FCA model risk management compliance readiness or benchmarking an AI governance program against UK regulatory expectations. Returns coverage across 13 control objectives from FCA Policy Statement PS7/24. Example: PS7/24 requires documented model validation methodology, ongoing performance monitoring, and board-level model risk appetite statement — gaps in any of the three trigger supervisory concern. Source: FCA Policy Statement PS7/24. |
| get_occ_enforcement_actions | Use when assessing regulatory risk for a national bank or federal thrift before a merger, acquisition, partnership, correspondent banking relationship, or vendor engagement. Returns active and historical OCC enforcement actions — formal agreements, consent orders, cease-and-desist orders, and civil money penalties — the same records OCC examiners pull during supervisory reviews. Example: First National Bank of Springfield — formal agreement active since March 2022 requiring BSA/AML program overhaul, independent compliance consultant, and quarterly progress reports to OCC — agreement not yet terminated, elevates acquisition risk materially. Source: OCC Enforcement Actions — official supervisory records. |
| get_sba_loan_market_data | Use when assessing small business lending opportunity in a market, benchmarking a bank's SBA production against competitors, evaluating CRA lending performance by geography, or identifying industries with unmet capital needs. Returns SBA 7(a) and 504 loan approval data — counts, amounts, average sizes, top lenders, and industry concentration by state and NAICS sector. Example: Illinois manufacturing sector — 847 SBA loans approved in 2023, $425K average, top 3 lenders holding 31% market share — 69% of market accessible to community bank competition. Source: SBA Public Loan Disclosure Data. |
| get_cra_performance_ratings | Use when evaluating a bank's Community Reinvestment Act track record before a merger application, charter acquisition, branch expansion approval, or community lending partnership. CRA ratings — Outstanding, Satisfactory, Needs to Improve, Substantial Noncompliance — are a primary federal approval factor for bank mergers and acquisitions. A 'Needs to Improve' rating can delay or block merger approval by 12-24 months. Example: Heartland Community Bank — Outstanding CRA rating, 2023 FDIC exam, fourth consecutive Outstanding — maximum approval runway for pending acquisition of Gateway Savings Bank. Source: FFIEC CRA Ratings Database — the official federal record. |
| get_federal_court_cases | Use when screening a company, executive, vendor, or counterparty for federal litigation exposure before a contract award, acquisition, investment, board appointment, or enterprise partnership. Returns active and historical federal court dockets across all US district and appellate courts — case names, docket numbers, courts, filing dates, nature of suit, and active status. Example: Acme Corp — 4 active federal cases: patent infringement N.D. Cal. (filed 2023), FLSA collective action S.D.N.Y. with 847 plaintiffs (filed 2023), FTC antitrust investigation D.D.C. (filed 2024), securities class action S.D.N.Y. (filed 2024) — aggregate litigation liability exposure estimated above $200M. Source: CourtListener, 1M+ federal court documents. |
| get_fec_campaign_finance | Federal campaign finance activity — PAC committees, total political disbursements, receipts, and political footprint signal. Source: FEC electronic filings. Use for political risk monitoring and PAC compliance. Cryptographically attested with a post-quantum signed settlement receipt. Verify at trust.stratalize.com/verify. |
| get_ftc_enforcement_history | Use when evaluating antitrust exposure, consumer protection liability, data privacy enforcement history, or deceptive practices risk for a company before an acquisition, strategic partnership, or enterprise vendor selection. FTC consent orders impose ongoing behavioral restrictions lasting 10-20 years and carry $50,000+ per day penalties for violations. Example: Tech Platform Corp — FTC consent order 2021, $150M civil penalty, 20-year restrictions on data monetization practices, biennial compliance reporting — restrictions survive acquisition and bind acquirer. Source: FTC Enforcement Cases and Proceedings. |
| get_dol_labor_violations | Use when screening an employer, vendor, or acquisition target for wage and hour compliance risk before a contract award, supply chain partnership, PE acquisition, or HR due diligence review. Returns DOL Wage and Hour Division enforcement history — FLSA overtime violations, minimum wage violations, child labor violations — with back wages assessed and employees affected. Repeat violations are a strong predictor of class action exposure. Example: Logistics Co LLC — 3 WHD investigations 2019-2023, $1.2M back wages, 891 employees affected for FLSA overtime violations — classified repeat violator, 340% higher class action probability vs first-time violators. Source: DOL WHISARD Enforcement Database. |
| get_colorado_ai_act_requirements | Use when building an AI governance compliance roadmap, advising on high-risk AI deployment obligations in Colorado, or briefing boards on upcoming US state AI regulatory requirements. Colorado SB 205 takes effect June 30, 2026 — the first comprehensive US state AI law. Returns developer and deployer obligations, high-risk AI system criteria, consumer rights, penalty structure ($20,000 per violation, AG enforcement), and comparison to EU AI Act. Example: AI-based loan underwriting system deployed in Colorado requires algorithmic impact assessment, plain-language consumer disclosure before first use, 3-year audit trail with AG access rights, and annual compliance certification — noncompliance triggers $20,000 per violation. Source: Colorado SB 205, enacted May 17, 2024. |
| get_nist_ai_rmf_requirements | Use when conducting an AI risk management gap assessment, building board-level AI governance documentation, preparing for a model risk examination, or aligning an AI program with federal regulatory expectations. NIST AI RMF 1.0 is the US federal standard for AI risk management — adopted by reference in the Executive Order on Safe AI and aligned with Federal Reserve SR 26-2, OCC model risk guidance, and FDIC requirements. Returns all four functions (GOVERN, MAP, MEASURE, MANAGE) with categories, subcategories, and implementation guidance. Example: GOVERN function requires board-level AI policy, documented accountability structures, and AI risk culture assessment — the first control examiners check in a model risk review. Source: NIST AI RMF 1.0. |
| get_us_state_ai_legislation | Use when mapping AI regulatory compliance obligations across multiple states, advising on jurisdiction-specific AI deployment requirements, or briefing legal and compliance teams on the US state AI legislation landscape. As of May 2026, Colorado (June 30), Illinois, Texas, California, Virginia, and 9 additional states have enacted or advanced material AI legislation — creating a patchwork of obligations for multi-state AI deployments without a federal standard. Example: Financial institution deploying AI in 12 states faces 4 distinct compliance regimes with conflicting definitions of high-risk AI — multi-state compliance cost estimated $800K-$2M annually for mid-size institutions. Source: NCSL + Stratalize Regulatory Intelligence. |
| get_model_risk_management_standards | Use when preparing for a model risk management examination, building an SR 26-2 compliant model governance program, or assessing a financial institution's MRM framework against regulatory expectations. Returns Federal Reserve SR 26-2 and OCC requirements across development, independent validation, ongoing monitoring, and governance — with exam deficiency rates showing where institutions most commonly fail. For AI and ML models, SR 26-2 explicitly requires independent validation even for vendor-supplied models and black-box systems. Example: Documentation deficiencies are the most common exam finding at 67% of reviewed institutions — inadequate conceptual soundness documentation for credit scoring models triggers immediate MRA (Matter Requiring Attention). Source: Federal Reserve SR 26-2, OCC Bulletin 2026-13, FDIC FIL-15-2026. |