mcp server

Stratalize Governance

AI governance intelligence: EU AI Act, FCA PS7/24, NIST AI RMF, OCC enforcement, and state AI laws.

Description as published by the maintainer. Source

• version 1.1.1
• archived

archived — The linked repository returns 404. It was deleted, renamed or made private.

What this server can do

18 functions, named and described by the server itself. Parameter names are shown because they say more about what a function does than its name usually does.

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_colorado_ai_act_requirements(system_type)

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_cra_performance_ratings(institution_name)

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. Required: institution_name.

get_dol_labor_violations(state, employer_name)

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. Required: employer_name.

get_eu_ai_act_coverage(nistFunction)

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_fec_campaign_finance(name)

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. Required: name.

get_federal_court_cases(court, party_name, years_back)

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. Required: party_name.

get_ftc_enforcement_history(company_name)

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. Required: company_name.

get_model_risk_management_standards(institution_type)

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.

get_nist_ai_rmf_requirements(function_filter)

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_occ_enforcement_actions(institution_name)

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. Required: institution_name.

get_ofac_sanctions_screening(alias, country, entity_name, entity_type)

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. Required: entity_name.

get_oig_exclusion_screening(npi, provider_name)

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(uei, cage_code, entity_name)

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_sba_loan_market_data(year, state, industry)

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_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_uk_fca_coverage(nistFunction)

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_us_state_ai_legislation(state)

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.

Last successful function declaration observed on 2026-08-13. Source: https://www.stratalize.com/api/mcp-public?vertical=governance. We list what the server declared; we do not call any of these functions.

Endpoint status observed on 2026-08-13. Source: https://www.stratalize.com/api/mcp-public?vertical=governance.

Signals

These are separate measurements of different things. They are deliberately not combined into one score, because a popularity number that mixes website traffic with saves and stars cannot be checked or acted on.

Where to get it

• Repository — https://github.com/Stratalize/Stratalize
• Website — https://www.stratalize.com
• Remote endpoint (streamable-http) — https://www.stratalize.com/api/mcp-public?vertical=governance

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