Regulatory Arbitrage Detection MCP

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Quick Start

Add to your MCP client (Claude Desktop, Cursor, Windsurf):

{
  "mcpServers": {
    "regulatory-arbitrage-detection-mcp": {
      "url": "https://ryanclinton--regulatory-arbitrage-detection-mcp.apify.actor/mcp"
    }
  }
}

---

Regulatory arbitrage detection for AI agents, compliance teams, and policy analysts — this MCP server quantifies cross-jurisdictional regulatory divergence using 8 mathematically rigorous tools backed by 16 live data sources. It connects to federal registers, corporate registries in 6 countries, financial regulators, and sanctions databases to answer questions no single API can: how far apart are two regulatory regimes, which compliance path is cheapest, and which regulators show signs of industry capture.

The server runs in Apify Standby mode and exposes a single /mcp endpoint compatible with Claude, Cursor, Windsurf, and any MCP client. Each tool call fires up to 16 Apify actors in parallel, builds a cross-jurisdictional regulatory network with Fellegi-Sunter entity resolution, and applies the appropriate algorithm — Sinkhorn-Knopp optimal transport, MILP branch-and-bound, supermodular game theory, Hawkes processes, or Bayesian structural time series — returning structured JSON you can act on immediately.

What data can you access?

| Data Point | Source | Example |
|---|---|---|
| 📋 Federal proposed rules and final rulemakings | Federal Register | "FDIC Capital Requirements Final Rule" |
| 🏛️ Congressional bills and legislative status | Congress Bill Tracker | "HR 4173 Dodd-Frank Act Section 619" |
| 📣 Consumer financial enforcement actions | CFPB Complaints | "Enforcement action against Pinnacle Lending LLC" |
| 🏢 UK corporate registrations and officer filings | UK Companies House | "Meridian Capital Partners Ltd — 3 active officers" |
| 🌍 Global corporate registry (140+ jurisdictions) | OpenCorporates | "Vantage Holdings GmbH, registered DE, active" |
| 🔗 Legal Entity Identifiers and ownership chains | GLEIF LEI | "549300VBOVDWLHXK8X87 — parent: Nexus Group SA" |
| 🍁 Canadian federal corporate registry | Canada Corporations | "Ironshore Technologies Inc — Manitoba, active" |
| 🥝 New Zealand corporate registry | NZ Companies | "Southpac Advisory Ltd — Auckland, NZ" |
| 🦘 Australian business registry | Australia ABN | "ABN 53 004 085 616 — Crestwood Logistics Pty Ltd" |
| 📈 SEC regulatory filings and disclosures | SEC EDGAR | "Form S-1: Deltaworks Financial Corp, 2024-03" |
| 🏦 US bank financial data and supervisory info | FDIC Bank Data | "Apex Community Bank — $2.1B assets, CAMELS 2" |
| 🚫 OFAC sanctions designations | OFAC | "SDN list: Borkowski Trading — UKRAINE-EO13685" |
| 🌐 Consolidated global sanctions and PEPs | OpenSanctions | "Politically exposed person — Ministry of Finance RU" |
| 🇪🇺 EU VAT registration verification | EU VAT | "DE285938503 — Rhineland Industrial AG, valid" |
| ™️ EU trademark registrations and status | EUIPO Trademark | "CTM 018234567 — 'TradePath' — Class 36 financial" |
| 💰 Federal government spending and contracts | USAspending | "Contract award: $48M — Clearfield Defense Systems" |

Why use Regulatory Arbitrage Detection MCP?

Compliance teams at multinational firms spend weeks manually comparing regulatory regimes before choosing a jurisdiction for a new entity or product. Law firms charge $15,000–$50,000 for regulatory gap analyses that go stale within months. Policy analysts lack the tools to distinguish genuine regulatory divergence from superficial differences in rule language.

This MCP automates the entire analytical pipeline. It collects live data from 16 government and regulatory sources, resolves entity identities across registries using probabilistic record linkage, and applies econometric and quantitative methods that academic researchers use — now available to any AI agent or developer with an API token.

- Standby mode — the server stays warm and responds in seconds, with no cold-start delay on each tool call
- Parallel data collection — up to 16 actors fire simultaneously, reducing data fetch time compared to sequential calls
- Pay-per-use — no subscription; each tool call costs $0.04–$0.06 with a spending limit you control
- MCP protocol — works natively with Claude Desktop, Cursor, Windsurf, Cline, and any HTTP MCP client
- Structured output — every tool returns typed JSON with statistical diagnostics, not free-form summaries

Features

- Sinkhorn-Knopp optimal transport — builds regulation probability distributions per jurisdiction, computes the Gibbs kernel K_ij = exp(-C_ij/epsilon) with entropic regularization, and alternates u/v scaling to convergence to produce pairwise Wasserstein distances and Sinkhorn divergences
- MILP compliance routing — formulates cross-jurisdictional compliance as a Mixed-Integer Linear Program minimizing total cost subject to binary jurisdiction selection variables; branch-and-bound enumeration finds the globally optimal route with compliance probability constraints
- Bipartite C4 capture detection — counts 4-cycles through each regulator node in a regulator-industry bipartite graph, compares to 100 Monte Carlo null model iterations (random bipartite graphs preserving degree sequences), and reports z-scores with revolving door counts and rule weakening rates
- Difference-in-Differences lobbying impact — partitions jurisdictions into treatment and control groups, computes the DiD treatment effect tau with standard error, t-statistic, and p-value, and validates the identification assumption via a parallel trends test
- Lempel-Ziv complexity estimation — applies LZ76 parsing (distinct substring count) to string representations of each jurisdiction's regulatory structure as a Kolmogorov complexity estimator; reports compression ratios and Gini coefficient of cross-jurisdictional complexity inequality
- Supermodular N-player game — models jurisdictions as players with payoff pi_i = alpha_ix_i - beta_ix_i^2 + gammax_isum(x_j); positive cross-derivative (strategic complements) guaranteed; best-response iteration converges to Nash equilibrium by Topkis' theorem; computes race-to-bottom index as welfare gap from Pareto optimum
- Hawkes process regulatory forecasting — models regulatory event intensity as lambda(t) = mu + sum alphabetaexp(-beta*(t-t_i)); self-exciting dynamics mean past events accelerate future ones; Ogata thinning generates forward simulations; branching ratio approaching 1 signals critical instability
- Bayesian Structural Time Series causal impact — Kalman filter state space model decomposes regulatory impact into level, trend, and seasonal components; counterfactual extrapolation estimates treatment effects with Bayesian credible intervals and posterior probabilities
- Fellegi-Sunter entity resolution — probabilistic record linkage across all 16 sources using Jaro-Winkler string similarity, m-probability/u-probability estimates, and a Neyman-Pearson decision rule; resolves the same entity appearing under different names in different registries
- 5 configurable data source categories — regulatory (Federal Register, Congress, CFPB), corporate (6 jurisdictions), financial (SEC, FDIC, sanctions), international (EU VAT, EUIPO), spending (USAspending); select only the sources relevant to your query to control cost

Use cases for regulatory arbitrage detection

Cross-border compliance strategy

Multinational legal and compliance teams need to know whether two regulatory regimes are genuinely different before committing to separate compliance programs. The measure_regulatory_distance tool quantifies divergence numerically: a Wasserstein distance near zero means harmonized compliance is viable; a distance above the 75th percentile signals material differences that require jurisdiction-specific programs. Teams feed these distances into their market-entry decision models.

Corporate regulatory risk assessment

Investment analysts and M&A advisors screening acquisition targets need to understand whether a target benefits from regulatory arbitrage that could evaporate after regulatory convergence. The model_regulatory_competition tool identifies whether a jurisdiction's favorable regulatory posture is a Nash equilibrium or an unstable position subject to competitive pressure from neighboring regulators.

Lobbying ROI and regulatory affairs benchmarking

Government affairs teams and policy research firms need evidence that lobbying expenditure produces measurable regulatory outcomes. The estimate_lobbying_impact tool runs a Difference-in-Differences analysis with parallel trends validation — the same methodology used in peer-reviewed policy journals — to produce a causal treatment effect estimate with a p-value, not just a correlation.

Regulatory capture and revolving door monitoring

Investigative journalists, think tanks, and watchdog organizations tracking regulatory capture need quantitative signals, not anecdotes. The detect_regulatory_capture tool measures the bipartite clustering coefficient C4 for each regulator-industry pair against a null model and flags cases where the z-score significantly exceeds random chance, alongside revolving door counts and rule weakening rates.

Regulatory change forecasting for business planning

Strategy teams at financial services firms, technology companies, and pharmaceutical groups need early warning of regulatory changes 30–90 days out. The predict_regulatory_changes tool fits a Hawkes self-exciting point process to historical regulatory events and simulates forward trajectories using Ogata thinning, flagging jurisdictions and sectors where event clustering signals imminent rulemaking.

GDPR and major regulation impact measurement

Policy teams and academic researchers studying whether a specific regulation caused measurable economic effects need rigorous causal inference, not before/after comparisons. The estimate_causal_regulation_impact tool applies Bayesian Structural Time Series with Kalman filtering to decompose the regulatory effect from underlying trends and seasonal patterns, reporting credible intervals and posterior probabilities.

How to use the Regulatory Arbitrage Detection MCP

1. Connect your MCP client — add the server URL https://regulatory-arbitrage-detection-mcp.apify.actor/mcp to your MCP client configuration (Claude Desktop, Cursor, or any HTTP MCP client). You will need an Apify API token.

2. Choose the right tool for your question — ask your AI agent a regulatory question in plain language. The agent selects the appropriate tool: distance measurement, compliance routing, capture detection, lobbying impact, complexity ranking, competition modeling, change forecasting, or causal impact estimation.

3. Narrow the data sources — by default, the regulatory, corporate, and financial source categories are active. For EU-focused work, add international. For government contracting analysis, add spending. Restricting sources reduces cost and response time.

4. Interpret the structured output — each tool returns JSON with model diagnostics (p-values, convergence flags, credible intervals) alongside the primary result. Statistical significance thresholds and confidence levels are included so you can assess reliability without further calculation.

MCP tools

| Tool | Price | Algorithm | Best for |
|------|-------|-----------|----------|
| measure_regulatory_distance | $0.045 | Sinkhorn-Knopp optimal transport | Quantifying divergence between two regulatory regimes |
| optimize_compliance_routing | $0.055 | MILP branch-and-bound | Finding the cheapest multi-jurisdiction compliance path |
| detect_regulatory_capture | $0.050 | Bipartite C4 + Monte Carlo null model | Screening regulators for industry capture signals |
| estimate_lobbying_impact | $0.045 | Difference-in-Differences with parallel trends | Measuring causal lobbying effects on regulatory outcomes |
| quantify_regulatory_complexity | $0.040 | Lempel-Ziv / LZ76 complexity | Comparing regulatory burden across jurisdictions |
| model_regulatory_competition | $0.045 | Supermodular N-player game (Topkis) | Predicting race-to-bottom dynamics |
| predict_regulatory_changes | $0.050 | Hawkes process + Ogata thinning | Forecasting imminent regulatory shifts |
| estimate_causal_regulation_impact | $0.050 | BSTS + Kalman filter | Measuring whether a regulation had measurable effects |

Tool input parameters

All 8 tools share the same two input parameters:

| Parameter | Type | Required | Default | Description |
|-----------|------|----------|---------|-------------|
| query | string | Yes | — | Regulatory query describing the jurisdiction, industry, or regulation type. Examples: "banking regulation EU US", "pharmaceutical lobbying drug approval", "GDPR impact on data companies" |
| sources | array | No | ["regulatory", "corporate", "financial"] | Data source categories to query. Options: "regulatory" (Federal Register, Congress, CFPB), "corporate" (UK, OpenCorporates, GLEIF, Canada, NZ, Australia), "financial" (SEC, FDIC, OFAC, OpenSanctions), "international" (EU VAT, EUIPO), "spending" (USAspending) |

Input examples

Measure regulatory distance between EU and US banking regimes:

{

  "query": "banking capital requirements EU US Basel III",

  "sources": ["regulatory", "corporate", "financial", "international"]

}

Detect regulatory capture in pharmaceutical sector:

{

  "query": "FDA pharmaceutical drug approval regulation",

  "sources": ["regulatory", "corporate", "financial"]

}

Predict cryptocurrency regulatory changes:

{

  "query": "cryptocurrency stablecoin regulation 2024",

  "sources": ["regulatory", "financial"]

}

Input tips

- Be specific in your query — include jurisdiction names, sector terms, and regulation names. "SEC broker-dealer capital requirements" returns sharper results than "financial regulation".
- Start with the default sources — regulatory, corporate, and financial cover most analyses. Add international for EU-specific work and spending only when government contracting is relevant to your question.
- Use the cheaper tools first — quantify_regulatory_complexity at $0.040 can orient you before running the $0.055 MILP routing optimization.
- Set a spending limit — configure a per-run budget in your Apify account to cap costs on exploratory queries.
- Run the full 8-tool suite for a thorough analysis — total cost is $0.38 per topic, covering distance, routing, capture, lobbying, complexity, competition, forecasting, and causal impact.

Output example

Response from measure_regulatory_distance for "banking regulation EU US":

{
  "pairCount": 3,
  "averageDistance": 0.312,
  "maxDistance": 0.487,
  "entropyRegularization": 0.05,
  "pairs": [
    {
      "jurisdictionA": "US",
      "jurisdictionB": "EU",
      "wassersteinDistance": 0.487,
      "sinkhornDivergence": 0.401,
      "sinkhornIterations": 47,
      "topDivergentRules": [
        "Basel III Tier 1 capital ratio",
        "CRD IV leverage ratio",
        "Volcker Rule proprietary trading ban",
        "DFAST stress testing frequency"
      ]
    },
    {
      "jurisdictionA": "US",
      "jurisdictionB": "UK",
      "wassersteinDistance": 0.298,
      "sinkhornDivergence": 0.241,
      "sinkhornIterations": 39,
      "topDivergentRules": [
        "PRA senior managers certification",
        "ring-fencing requirements",
        "FPC systemic risk buffer"
      ]
    },
    {
      "jurisdictionA": "EU",
      "jurisdictionB": "UK",
      "wassersteinDistance": 0.151,
      "sinkhornDivergence": 0.132,
      "sinkhornIterations": 28,
      "topDivergentRules": [
        "MiFID II transaction reporting",
        "SMCR vs EBA guidelines"
      ]
    }
  ],
  "transportPlanSize": 9,
  "nodeCount": 84,
  "edgeCount": 217,
  "entityResolution": {
    "matchCount": 12,
    "possibleCount": 4
  }
}

Response from detect_regulatory_capture for "SEC financial regulation":

{
  "overallCaptureRisk": 0.61,
  "bipartiteModularity": 0.43,
  "nullModelIterations": 100,
  "signals": [
    {
      "regulator": "SEC Division of Trading & Markets",
      "sector": "broker-dealer",
      "c4Score": 0.78,
      "revolvingDoorCount": 14,
      "ruleWeakeningRate": 0.31,
      "captureRisk": "HIGH",
      "nullModelC4": 0.22,
      "zScore": 2.91
    },
    {
      "regulator": "CFTC Division of Market Oversight",
      "sector": "derivatives",
      "c4Score": 0.54,
      "revolvingDoorCount": 8,
      "ruleWeakeningRate": 0.19,
      "captureRisk": "MEDIUM",
      "nullModelC4": 0.21,
      "zScore": 1.62
    }
  ],
  "criticalSignals": [],
  "nodeCount": 91
}

Response from predict_regulatory_changes for "cryptocurrency stablecoin":

{
  "averageRate": 2.4,
  "clusteringCoefficient": 0.68,
  "logLikelihood": -34.2,
  "predictions": [
    {
      "jurisdiction": "US",
      "sector": "stablecoin",
      "expectedTime": 34,
      "intensity": 3.1,
      "direction": "TIGHTENING",
      "confidence": 0.81,
      "baselineRate": 1.2,
      "kernelContribution": 1.9
    },
    {
      "jurisdiction": "EU",
      "sector": "crypto-assets",
      "expectedTime": 61,
      "intensity": 1.8,
      "direction": "TIGHTENING",
      "confidence": 0.67,
      "baselineRate": 0.9,
      "kernelContribution": 0.9
    }
  ],
  "imminentChanges": [
    {
      "jurisdiction": "US",
      "sector": "stablecoin",
      "expectedTime": 34,
      "direction": "TIGHTENING",
      "confidence": 0.81
    }
  ],
  "ogataResidualCount": 22,
  "nodeCount": 67
}

Output fields

measure_regulatory_distance

| Field | Type | Description |
|-------|------|-------------|
| pairCount | number | Number of jurisdiction pairs compared |
| averageDistance | number | Mean Wasserstein distance across all pairs |
| maxDistance | number | Maximum pairwise distance (largest divergence) |
| entropyRegularization | number | Epsilon parameter used in Sinkhorn iterations |
| pairs[].jurisdictionA | string | First jurisdiction in pair |
| pairs[].jurisdictionB | string | Second jurisdiction in pair |
| pairs[].wassersteinDistance | number | Optimal transport distance (0 = identical, 1 = maximally divergent) |
| pairs[].sinkhornDivergence | number | Regularized divergence measure |
| pairs[].sinkhornIterations | number | Iterations to convergence |
| pairs[].topDivergentRules | string[] | Regulations contributing most to the distance |
| transportPlanSize | number | Dimensions of the optimal transport plan matrix |
| nodeCount | number | Regulatory network nodes used in analysis |
| edgeCount | number | Regulatory network edges used in analysis |
| entityResolution.matchCount | number | Entity pairs confirmed as same entity across sources |
| entityResolution.possibleCount | number | Entity pairs flagged as possible matches for review |

detect_regulatory_capture

| Field | Type | Description |
|-------|------|-------------|
| overallCaptureRisk | number | Aggregate capture risk score 0–1 |
| bipartiteModularity | number | Community structure strength in regulator-industry graph |
| nullModelIterations | number | Monte Carlo iterations used for null model (always 100) |
| signals[].regulator | string | Regulatory body assessed |
| signals[].sector | string | Industry sector in the regulator-industry pair |
| signals[].c4Score | number | Bipartite 4-cycle clustering coefficient |
| signals[].nullModelC4 | number | Mean C4 from null model for comparison |
| signals[].zScore | number | Standard deviations above null model; values >2 are notable |
| signals[].revolvingDoorCount | number | Personnel movements between regulator and regulated industry |
| signals[].ruleWeakeningRate | number | Proportion of rule changes that weakened requirements |
| signals[].captureRisk | string | LOW, MEDIUM, HIGH, or CRITICAL |

optimize_compliance_routing

| Field | Type | Description |
|-------|------|-------------|
| optimalRoute.path | string[] | Ordered list of jurisdictions in the optimal compliance path |
| optimalRoute.totalCost | number | Total compliance cost for this route |
| optimalRoute.complianceProbability | number | Probability of full compliance on this path |
| optimalRoute.riskScore | number | Aggregate risk score for the route |
| optimalRoute.bottleneck | string | Jurisdiction or rule creating the binding constraint |
| milpObjective | number | Optimal MILP objective value |
| totalVariables | number | Number of binary decision variables in the MILP |
| branchAndBoundNodes | number | Nodes explored in branch-and-bound enumeration |
| constraintsSatisfied | number | Number of compliance constraints satisfied by optimal route |
| topRoutes | array | Top 10 routes by cost for comparison |

estimate_lobbying_impact

| Field | Type | Description |
|-------|------|-------------|
| treatmentEffect | number | DiD estimate of lobbying's causal effect (tau) |
| standardError | number | Standard error of the treatment effect estimate |
| tStatistic | number | t-statistic for hypothesis test |
| pValue | number | p-value; values below 0.05 indicate statistical significance |
| significant | boolean | Whether p-value < 0.05 |
| parallelTrendsTestPassed | boolean | Whether the identification assumption holds |
| treatmentGroup.pre | number | Pre-treatment mean for lobbying jurisdictions |
| treatmentGroup.post | number | Post-treatment mean for lobbying jurisdictions |
| controlGroup.pre | number | Pre-treatment mean for control jurisdictions |
| controlGroup.post | number | Post-treatment mean for control jurisdictions |
| sampleSize | number | Total observations in the DiD regression |

quantify_regulatory_complexity

| Field | Type | Description |
|-------|------|-------------|
| averageComplexity | number | Mean LZ76 complexity across jurisdictions |
| maxComplexity | number | Highest complexity score observed |
| giniCoefficient | number | Inequality of complexity distribution across jurisdictions |
| complexityIndex | number | Composite normalized index (0–1) |
| entries[].jurisdiction | string | Jurisdiction assessed |
| entries[].lzComplexity | number | Raw LZ76 distinct substring count |
| entries[].compressionRatio | number | Compression ratio of regulatory text representation |
| entries[].normalizedComplexity | number | Complexity normalized to 0–1 scale |
| entries[].ruleCount | number | Number of distinct rules in this jurisdiction |
| entries[].crossReferences | number | Cross-references between rules |
| entries[].tier | string | LOW, MEDIUM, HIGH, or EXTREME |

predict_regulatory_changes

| Field | Type | Description |
|-------|------|-------------|
| averageRate | number | Baseline regulatory event rate (events per month) |
| clusteringCoefficient | number | Hawkes self-excitation ratio (approaches 1 = critical instability) |
| logLikelihood | number | Hawkes process log-likelihood (model fit) |
| predictions[].jurisdiction | string | Jurisdiction where change is expected |
| predictions[].sector | string | Regulatory sector affected |
| predictions[].expectedTime | number | Expected days until next regulatory event |
| predictions[].intensity | number | Expected event intensity at predicted time |
| predictions[].direction | string | TIGHTENING, LOOSENING, or STABLE |
| predictions[].confidence | number | Model confidence 0–1 |
| imminentChanges | array | Predictions with expectedTime < 90 days |
| ogataResidualCount | number | Residual count from Ogata thinning simulation |

estimate_causal_regulation_impact

| Field | Type | Description |
|-------|------|-------------|
| averageEffect | number | Mean causal effect across all regulations assessed |
| significantCount | number | Number of regulations with statistically significant effects |
| totalAssessed | number | Total regulations included in analysis |
| logLikelihood | number | BSTS model log-likelihood |
| kalmanGainSample | number[] | Last 5 Kalman gain values (tracks filter convergence) |
| entries[].regulation | string | Regulation assessed |
| entries[].jurisdiction | string | Jurisdiction of the regulation |
| entries[].pointEffect | number | Point estimate of causal effect |
| entries[].cumulativeEffect | number | Cumulative effect over the post-treatment window |
| entries[].credibleIntervalLower | number | Lower bound of Bayesian credible interval |
| entries[].credibleIntervalUpper | number | Upper bound of Bayesian credible interval |
| entries[].posterior_p | number | Posterior probability that effect is non-zero |
| entries[].significant | boolean | Whether posterior probability exceeds 0.95 |
| entries[].trendComponent | number | Kalman-estimated trend component |
| entries[].seasonalComponent | number | Kalman-estimated seasonal component |

How much does it cost to use regulatory arbitrage detection?

This MCP uses pay-per-event pricing — you pay per tool call. Compute costs are included in the price. There are no subscriptions or minimum commitments.

| Scenario | Tool calls | Cost per call | Total cost |
|----------|-----------|---------------|------------|
| Quick test — one distance measurement | 1 | $0.045 | $0.045 |
| Single analysis — one topic, one tool | 1 | $0.040–$0.055 | $0.04–$0.06 |
| Topic survey — 4 tools on one regulatory area | 4 | $0.045 avg | ~$0.19 |
| Full suite — all 8 tools on one topic | 8 | $0.046 avg | ~$0.38 |
| Weekly monitoring — 5 topics per week | 40 | $0.046 avg | ~$1.84/week |

You can set a maximum spending limit per run in Apify account settings to control costs. The server returns a clear error message when your budget is reached rather than silently failing.

The Apify Free plan includes $5 of monthly platform credits — enough for 100+ individual tool calls before you need to add payment.

Compare this to a law firm regulatory gap analysis at $15,000–$50,000 per engagement, or regulatory intelligence subscription services at $2,000–$8,000 per month.

How to connect this MCP server

Claude Desktop

Add to your claude_desktop_config.json:

{
  "mcpServers": {
    "regulatory-arbitrage-detection": {
      "url": "https://regulatory-arbitrage-detection-mcp.apify.actor/mcp",
      "headers": {
        "Authorization": "Bearer YOUR_APIFY_TOKEN"
      }
    }
  }
}

Cursor / Windsurf / Cline

Add a new MCP server entry pointing to:

https://regulatory-arbitrage-detection-mcp.apify.actor/mcp

Python (direct HTTP)

import requests
response = requests.post(

    "https://regulatory-arbitrage-detection-mcp.apify.actor/mcp",

    headers={

        "Content-Type": "application/json",

        "Authorization": "Bearer YOUR_APIFY_TOKEN"

    },

    json={

        "jsonrpc": "2.0",

        "method": "tools/call",

        "params": {

            "name": "measure_regulatory_distance",

            "arguments": {

                "query": "banking capital requirements EU US Basel III",

                "sources": ["regulatory", "corporate", "financial", "international"]

            }

        },

        "id": 1

    }

)
result = response.json()

distance_data = result["result"]["content"][0]["text"]

print(f"Regulatory distance analysis: {distance_data}")

JavaScript / TypeScript

const response = await fetch(
  "https://regulatory-arbitrage-detection-mcp.apify.actor/mcp",
  {
    method: "POST",
    headers: {
      "Content-Type": "application/json",
      "Authorization": "Bearer YOUR_APIFY_TOKEN"
    },
    body: JSON.stringify({
      jsonrpc: "2.0",
      method: "tools/call",
      params: {
        name: "detect_regulatory_capture",
        arguments: {
          query: "SEC financial regulation broker-dealer",
          sources: ["regulatory", "corporate", "financial"]
        }
      },
      id: 1
    })
  }
);
const data = await response.json();

const captureResult = JSON.parse(data.result.content[0].text);

console.log(Overall capture risk: ${captureResult.overallCaptureRisk});

console.log(High/critical signals: ${captureResult.criticalSignals.length});

cURL

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