UK Regulatory Ecosystem MCP

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Quick Start

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

{
  "mcpServers": {
    "uk-regulatory-ecosystem-mcp": {
      "url": "https://ryanclinton--uk-regulatory-ecosystem-mcp.apify.actor/mcp"
    }
  }
}

---

UK regulatory intelligence across 8 government databases — this MCP server gives AI agents a single interface to query Companies House, UK Police crime data, Food Standards Agency hygiene ratings, Land Registry, Charity Commission, Environment Agency waste carriers, flood warnings, and NSTA oil/gas licensing simultaneously. It is built for compliance teams, due diligence analysts, and AI workflows that need to assess UK entity risk without manually querying eight separate government portals.

The server orchestrates 8 Apify actors in parallel, constructs a cross-agency regulatory network graph, and runs 8 analysis tools ranging from KDE-based geographic risk heat mapping to multi-factor weighted risk scoring. Every tool call returns structured JSON with ranked risk scores, compliance flags, and actionable recommendations. Data is fetched live with a 5-minute in-memory cache to prevent redundant actor calls within a session.

What data can you access?

| Data Point | Source | Example |
|---|---|---|
| 📋 Company registration status, officers, filings, PSC | UK Companies House | Pinnacle Logistics Ltd — Active, incorporated 2018 |
| 🚔 Street-level crime categories and outcomes | UK Police Crime Data | Burglary — Thetford St, outcome: charged |
| 🍽️ Food hygiene ratings 0–5 with inspection dates | Food Standards Agency | Harbour Kitchen — Rating 2, last inspected Oct 2024 |
| 🏠 Property ownership, titles, tenure, price paid | UK Land Registry | 14 Colmore Row — Freehold, £2.4M, Jan 2023 |
| 🎗️ Charity registration validity, income, trustee data | Charity Commission | Westfield Community Trust — Registered, income £84k |
| ♻️ Waste carrier registration status and tier | Environment Agency | Apex Clearance Ltd — Upper Tier, expires 2026-03 |
| 🌊 Active flood warnings, alerts, and zone data | UK Flood Warnings | River Wye — Severe warning, raised 06:14 |
| ⛽ Oil and gas exploration/production licences | NSTA | PEDL 234 — Active, operator: Serica Energy |
| 🔗 Cross-agency entity connections in network graph | All 8 sources | Pinnacle Logistics linked to 3 agencies, risk 0.74 |
| 🌡️ KDE composite geographic risk score per area | Crime + Flood + Env | SE14 3AH — compositeRisk: 0.81, highRisk: true |

Why use UK Regulatory Ecosystem MCP?

Checking a UK company across all relevant regulatory databases manually requires opening eight different government websites, searching each one separately, and stitching results together by hand. For a single acquisition target, that typically takes 4–6 hours per analyst. For bulk screening — supply chains, charity registers, landlord portfolios — it scales to weeks.

This MCP server automates the entire process. One tool call triggers parallel queries across all selected data sources, builds a regulatory network graph linking entities by location and type, and runs whichever analysis algorithm the tool implements — compliance scoring, cross-agency violation detection, geographic heat mapping, or full report generation.

Beyond saving time, the cross-agency analysis surface risks that no single-source check would catch. A waste carrier with an expired registration that also operates near a crime hotspot and has a food hygiene rating of 1 presents compounding risk that only becomes visible when all three sources are queried together.

Platform benefits of running on Apify:
- Scheduling — run daily or weekly compliance sweeps on watchlists to catch status changes
- API access — trigger analysis from Python, JavaScript, or any HTTP client with a single token
- Spend controls — set a maximum per-run budget; the server stops charging when the limit is reached
- Monitoring — receive Slack or email alerts when runs fail or return unexpected results
- Integrations — connect output to Zapier, Make, Google Sheets, HubSpot, or downstream webhooks

Features

- 8 government data sources in one interface — Companies House, Police, FSA, Land Registry, Charity Commission, Environment Agency, Flood Warnings, and NSTA, all queryable from a single MCP tool call
- Parallel actor orchestration with 5-minute cache — the resolveData() function fans out up to 8 Apify actor calls concurrently using Promise.all(), with per-source TTL caching to avoid redundant charges within a session
- Regulatory network graph construction — buildRegNetwork() assembles entities from all sources into typed nodes (company, charity, food_business, waste_carrier, flood_zone, oil_license, crime_area, property) and creates co-location edges using 15-character location prefix matching
- KDE-based geographic risk heat mapping — computeGeographicRiskHeat() applies a Gaussian kernel (bandwidth = 0.5 degrees) to smooth crime, flood, and environmental risk signals across geographic cells, producing composite risk scores weighted 40% crime / 35% flood / 25% environmental
- Weighted multi-factor risk scoring — scoreMultiRegulatoryRisk() computes entity risk using 6 weighted factors: registry status (0.20), food hygiene (0.15), crime proximity (0.20), flood exposure (0.15), environmental compliance (0.15), network anomaly (0.15)
- Cross-agency violation detection — identifies entities simultaneously flagged by UK Police, Food Standards Agency, Environment Agency, and Flood Authority, classifying violations as dual_agency_flag or multi_agency_convergence
- Company compliance assessment — evaluates Companies House active status, linked food hygiene scores, waste carrier validity, and crime proximity into a 0–1 overall compliance score
- Charity legitimacy screening — detects revenue anomalies against population median, corporate director overlap by address, and registration status gaps, with scored red flag accumulation
- Environmental compliance analysis — assesses waste carrier active/expired status, oil/gas licence validity from NSTA, and flood zone edge proximity (0.3 risk increment per flood edge, capped at 1.0)
- Comprehensive regulatory report generation — generateRegulatoryReport() produces structured findings per category (Companies House, Food Standards, Environment Agency, Flood Risk, Police) with severity ratings (low / medium / high / critical) and specific escalation recommendations
- Per-tool pay-per-event pricing — 6 tools at $0.045/call, 2 tools (detect_cross_agency_violations and generate_regulatory_report) at $0.050/call; spending limit enforced server-side
- Dual transport support — serves both SSE (/sse + /messages) for legacy MCP clients and StreamableHTTP (/mcp) for modern clients, with a /health endpoint for readiness checks

Use cases for UK regulatory intelligence

Due diligence for UK acquisitions and investments

M&A analysts and private equity teams need to screen acquisition targets across all relevant UK registries before signing. A single generate_regulatory_report call against the target company name surfaces dissolved subsidiaries, environmental liabilities, food hygiene issues in operating premises, flood zone exposure across owned properties, and any cross-agency flags — all in one structured report, ready to paste into a due diligence memo.

Supply chain compliance monitoring

Procurement teams managing UK supplier networks need ongoing assurance that waste carriers hold valid Environment Agency registrations, food suppliers maintain acceptable FHRS ratings, and no supplier has newly dissolved a key operating entity. Scheduling a weekly assess_company_compliance sweep across a supplier watchlist provides continuous monitoring without manual portal checks.

Charity fraud and governance screening

Grant-making bodies, regulators, and investigative journalists need to verify that charities receiving public money are legitimately registered, have plausible income patterns, and lack suspicious director overlap with commercial entities. The screen_charity_legitimacy tool compares each charity's income against the population median, flags corporate co-location overlap, and produces a 0–1 legitimacy score with named red flags.

Geographic risk assessment for property investment

Real estate investors and urban planners evaluating UK locations need to understand the combined risk profile of an area — not just flood risk in isolation, but the intersection of crime density, environmental contamination from waste operators, and active flood warnings. The compute_geographic_risk_heat tool produces KDE-smoothed composite risk scores per geographic cell, enabling area-to-area comparison before committing capital.

Regulatory compliance AI agents

Compliance teams building AI agent workflows in Claude, Cursor, or other MCP-compatible clients can wire this server into their assistants to answer natural-language queries like "Is Meridian Waste Solutions compliant across all UK registries?" or "Which of our 50 suppliers are in high flood-risk zones?" The MCP protocol handles tool discovery and structured response formatting automatically.

Environmental enforcement support

Environment Agency regional teams, local authority environmental health officers, and NGOs monitoring illegal waste activity can use detect_cross_agency_violations to surface operators simultaneously appearing in crime data and waste carrier records with expired registrations — the combination that most commonly indicates fly-tipping or illegal waste treatment operations.

How to connect this MCP server to your AI client

Step 1: Get your Apify API token

Sign up at apify.com and copy your API token from Account Settings.

Step 2: Add to your MCP client

Claude Desktop — add to claude_desktop_config.json:

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

Cursor / Windsurf / Cline — add the same URL and Authorization header in your MCP server settings panel.

Step 3: Start querying

Once connected, your AI client discovers all 8 tools automatically. Ask questions in natural language:

> "Assess the regulatory compliance of Apex Industrial Services Ltd across all UK sources."

> "Generate a regulatory report for businesses in the SW1A 1AA postcode area."

Step 4: Review structured results

Each tool returns a JSON object with ranked entities, risk scores, compliance flags, and recommendations. Results can be piped into a report, stored to a dataset, or used as context for downstream AI reasoning.

MCP tools

| Tool | Price | Default Sources | Description |
|------|-------|-----------------|-------------|
| map_regulatory_network | $0.045 | companies, police, food, land, charity | Map entity connectivity across agencies with network density and risk score |
| assess_company_compliance | $0.045 | companies, police, food, land, charity | Score companies on registry status, food hygiene, waste carrier validity, crime proximity |
| detect_cross_agency_violations | $0.050 | companies, police, food, waste, flood | Identify entities flagged by 2+ agencies simultaneously |
| compute_geographic_risk_heat | $0.045 | police, flood, waste, energy | KDE heat map of composite geographic risk per area |
| screen_charity_legitimacy | $0.045 | charity, companies | Screen for revenue anomalies, corporate overlap, registration gaps |
| analyze_environmental_compliance | $0.045 | companies, waste, flood, energy | Assess waste carrier status, oil/gas licence, flood zone exposure |
| score_multi_regulatory_risk | $0.045 | companies, police, food, land, charity, waste, flood | Six-factor weighted composite risk score per entity |
| generate_regulatory_report | $0.050 | all 8 sources | Full findings report with severity ratings and escalation recommendations |

Tool input parameters

Every tool accepts the same two parameters:

| Parameter | Type | Required | Default | Description |
|-----------|------|----------|---------|-------------|
| query | string | Yes | — | UK company name, location, postcode, or entity to investigate |
| sources | array of enum | No | Tool-specific (see table above) | Data sources to query: companies, police, food, land, charity, waste, flood, energy |

Input examples

Company compliance check (default sources):

{

  "query": "Meridian Waste Solutions Ltd",

  "sources": ["companies", "police", "food", "waste"]

}

Geographic risk assessment for a postcode:

{

  "query": "E14 5AB",

  "sources": ["police", "flood", "waste", "energy"]

}

Full regulatory report with all 8 sources:

{

  "query": "Pinnacle Food Group",

  "sources": ["companies", "police", "food", "land", "charity", "waste", "flood", "energy"]

}

Input tips

- Use the company's registered name — search terms are passed directly to each source's API; the full legal name (as registered at Companies House) returns the most accurate results
- Start with 3–4 sources — the default source sets for each tool are optimised for that tool's purpose; expand to all 8 only when you need the comprehensive report
- Use postcode queries for geographic tools — compute_geographic_risk_heat and detect_cross_agency_violations return richer results when queried with a postcode or area name rather than a company name
- Combine tools in sequence — run score_multi_regulatory_risk first to identify the highest-risk entities, then call generate_regulatory_report only on those flagged above 0.5

Output examples

assess_company_compliance output

{
  "checks": [
    {
      "company": "Pinnacle Food Group Ltd",
      "registryStatus": "Active",
      "foodHygieneScore": 2.4,
      "wasteCarrierValid": false,
      "crimeProximity": 0.4,
      "overallCompliance": 0.3800
    },
    {
      "company": "Meridian Catering Services",
      "registryStatus": "Active",
      "foodHygieneScore": 4.2,
      "wasteCarrierValid": true,
      "crimeProximity": 0.0,
      "overallCompliance": 0.8600
    }
  ],
  "totalChecked": 2,
  "failedCount": 1,
  "avgCompliance": 0.6200
}

detect_cross_agency_violations output

{
  "violations": [
    {
      "entity": "Apex Clearance Ltd",
      "agencies": ["UK Police", "Environment Agency", "Food Standards Agency"],
      "violationType": "multi_agency_convergence",
      "severity": 0.7500,
      "evidence": "Entity flagged across 3 regulatory agencies"
    }
  ],
  "totalDetected": 1,
  "multiAgencyCount": 1,
  "avgSeverity": 0.7500
}

generate_regulatory_report output

{
  "findings": [
    {
      "category": "Companies House",
      "finding": "2 dissolved/liquidated company(ies) with regulatory links",
      "severity": "high",
      "evidence": "Apex Waste UK, Meridian En",
      "recommendation": "Report to Companies House and investigate potential phoenix company fraud"
    },
    {
      "category": "Food Standards",
      "finding": "3 food business(es) with hygiene rating <= 2",
      "severity": "high",
      "evidence": "Harbour Kitchen (1), Eastgate Cafe (2), The Diner (2)",
      "recommendation": "Escalate to local authority food safety team for urgent inspection"
    },
    {
      "category": "Environment Agency",
      "finding": "1 waste carrier(s) with non-active registration",
      "severity": "high",
      "evidence": "Apex Clearance Ltd",
      "recommendation": "Report to Environment Agency for illegal waste carriage investigation"
    }
  ],
  "overallRisk": "high",
  "entitiesScreened": 24,
  "recommendations": [
    "Companies House referral for dissolved entity activity",
    "Food Standards Agency urgent inspection programme",
    "Environment Agency enforcement action for unlicensed waste carriers"
  ]
}

compute_geographic_risk_heat output

{
  "cells": [
    {
      "area": "Silvertown Way, E16",
      "lat": 51.5074,
      "lng": 0.0123,
      "crimeRisk": 0.7200,
      "floodRisk": 0.8100,
      "environmentalRisk": 0.4500,
      "compositeRisk": 0.6955
    },
    {
      "area": "Thames Road, DA8",
      "lat": 51.4892,
      "lng": 0.1834,
      "crimeRisk": 0.3100,
      "floodRisk": 0.6400,
      "environmentalRisk": 0.2800,
      "compositeRisk": 0.4700
    }
  ],
  "totalAreas": 14,
  "highRiskCount": 3,
  "avgCompositeRisk": 0.5210
}

Output fields

assess_company_compliance

| Field | Type | Description |
|-------|------|-------------|
| checks[].company | string | Company name (up to 50 chars) |
| checks[].registryStatus | string | Companies House status string (e.g. Active, Dissolved) |
| checks[].foodHygieneScore | number | Average FHRS score 0–5 for linked food premises |
| checks[].wasteCarrierValid | boolean | True if no waste carrier links, or at least one is Active |
| checks[].crimeProximity | number | 0–1 score; increments 0.2 per linked crime area node |
| checks[].overallCompliance | number | 0–1 weighted composite (registry 0.3, food 0.3, waste 0.2, crime 0.2) |
| totalChecked | number | Count of company nodes analysed |
| failedCount | number | Count with overallCompliance < 0.5 |
| avgCompliance | number | Mean overallCompliance across all checks |

detect_cross_agency_violations

| Field | Type | Description |
|-------|------|-------------|
| violations[].entity | string | Entity name |
| violations[].agencies | string[] | Agency names where the entity is flagged |
| violations[].violationType | string | dual_agency_flag (2 agencies) or multi_agency_convergence (3+) |
| violations[].severity | number | 0–1 severity score |
| violations[].evidence | string | Human-readable evidence string |
| totalDetected | number | Total violations found |
| multiAgencyCount | number | Violations involving 3+ agencies |
| avgSeverity | number | Mean severity across all violations |

compute_geographic_risk_heat

| Field | Type | Description |
|-------|------|-------------|
| cells[].area | string | Location string (up to 20 chars) |
| cells[].lat | number | Latitude (4 decimal places) |
| cells[].lng | number | Longitude (4 decimal places) |
| cells[].crimeRisk | number | 0–1 KDE-smoothed crime density |
| cells[].floodRisk | number | 0–1 KDE-smoothed flood risk |
| cells[].environmentalRisk | number | 0–1 KDE-smoothed environmental risk |
| cells[].compositeRisk | number | Weighted composite: 0.4×crime + 0.35×flood + 0.25×env |
| totalAreas | number | Total geographic cells in output |
| highRiskCount | number | Cells with compositeRisk > 0.6 |
| avgCompositeRisk | number | Mean compositeRisk across all cells |

generate_regulatory_report

| Field | Type | Description |
|-------|------|-------------|
| findings[].category | string | Regulatory category (e.g. Companies House, Food Standards) |
| findings[].finding | string | Descriptive finding text |
| findings[].severity | string | low, medium, high, or critical |
| findings[].evidence | string | Supporting evidence string |
| findings[].recommendation | string | Specific escalation action |
| overallRisk | string | Aggregate risk: low, medium, high, or critical |
| entitiesScreened | number | Total nodes in regulatory network |
| recommendations | string[] | Prioritised list of actions |

score_multi_regulatory_risk

| Field | Type | Description |
|-------|------|-------------|
| assessments[].entity | string | Entity name |
| assessments[].riskScore | number | 0–1 weighted composite risk |
| assessments[].riskLevel | string | low (<0.25), medium (<0.5), high (<0.75), critical (≥0.75) |
| assessments[].factors[].factor | string | Factor name (registry_status, food_hygiene, crime_proximity, flood_exposure, environmental, network_anomaly) |
| assessments[].factors[].weight | number | Factor weight in composite score |
| assessments[].factors[].score | number | Raw factor score 0–1 |
| avgRisk | number | Mean riskScore across all entities |
| criticalCount | number | Count with riskLevel = critical |

How much does it cost to run UK regulatory intelligence queries?

This MCP uses pay-per-event pricing — you pay per tool call. Six tools cost $0.045 per call, and two tools (detect_cross_agency_violations and generate_regulatory_report) cost $0.050 per call. Platform compute costs are included.

| Scenario | Tool calls | Cost per call | Total cost |
|----------|-----------|---------------|------------|
| Single company check | 1 | $0.045 | $0.045 |
| Compliance + violations (2 tools) | 2 | $0.045–$0.050 | ~$0.09 |
| Full 8-tool analysis of one entity | 8 | $0.045–$0.050 | ~$0.37 |
| Weekly sweep of 20 suppliers (1 tool each) | 20 | $0.045 | $0.90 |
| Monthly due diligence batch (50 entities) | 50 | $0.045 | $2.25 |

You can set a maximum spending limit per run to control costs. The server returns a spending-limit-reached message and stops charging when your budget is reached.

The Apify Free plan includes $5 of monthly platform credits, covering approximately 110 tool calls. Most individual due diligence workflows cost under $0.50. Compare this to commercial compliance platforms that charge $500–2,000/month for similar UK registry coverage — with this MCP, most teams spend $5–30/month with no subscription commitment.

Using UK regulatory intelligence via the API

Python

from apify_client import ApifyClient
client = ApifyClient("YOUR_API_TOKEN")
run = client.actor("ryanclinton/uk-regulatory-ecosystem-mcp").call(run_input={})
for item in client.dataset(run["defaultDatasetId"]).iterate_items():

    print(f"Tool: {item.get('tool')} | Result: {item.get('result')}")

JavaScript

import { ApifyClient } from "apify-client";
const client = new ApifyClient({ token: "YOUR_API_TOKEN" });
const run = await client.actor("ryanclinton/uk-regulatory-ecosystem-mcp").call({});
const { items } = await client.dataset(run.defaultDatasetId).listItems();

for (const item of items) {

    console.log(Tool: ${item.tool} | Overall Risk: ${item.result?.overallRisk});

}

cURL (direct MCP protocol — Streamable HTTP)

```bash