Aviation Safety Risk MCP Server

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Quick Start

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

{
  "mcpServers": {
    "aviation-safety-risk-mcp": {
      "url": "https://ryanclinton--aviation-safety-risk-mcp.apify.actor/mcp"
    }
  }
}

---

Aviation safety intelligence for AI agents — this MCP server queries 8 live government data sources in parallel and returns a composite Aviation Safety Score (0-100) covering airline safety defects, airworthiness directives, route weather risk, sanctions exposure, and maintenance compliance. Built for aviation insurers, MRO providers, aircraft lessors, and compliance teams who need structured risk data inside their AI workflows.

The server exposes 8 MCP tools that run automatically when called from Claude, Cursor, Windsurf, or any MCP-compatible agent. Each tool dispatches parallel queries across NHTSA safety records, NOAA weather alerts, FEMA disaster data, OFAC and OpenSanctions watchlists, Federal Register airworthiness directives, OpenCorporates corporate registries, and Data.gov aviation datasets — then applies scoring algorithms to return a structured risk report in under 120 seconds.

What data can you access?

| Data Point | Source | Example |
|---|---|---|
| 📋 Safety defect and recall patterns | NHTSA Vehicle Safety | Engine/fuel/structural defect records |
| ⛈️ Route weather threats | NOAA Weather Alerts | Thunderstorm, icing, wind shear, volcanic ash |
| 🌪️ Disaster events affecting airports | FEMA Disaster Alerts | Hurricane, earthquake, tornado declarations |
| 🚫 OFAC SDN sanctions matches | OFAC Sanctions Search | SDN match score, entity type, SDN entry |
| 🌐 Multi-jurisdiction watchlist hits | OpenSanctions Search | 100+ international sanctions programs |
| 🏢 Airline ownership and corporate structure | OpenCorporates | 140+ jurisdiction corporate registries |
| 📜 Airworthiness directives and safety rulemakings | Federal Register | Emergency ADs, mandatory inspections |
| 🗄️ Government aviation safety datasets | Data.gov | NTSB incident data, FAA safety records |
| 🔢 Composite Aviation Safety Score | Scoring engine | 0-100 risk score, 5-tier verdict |
| ⚠️ Actionable risk signals | All sources | Plain-English signal list per finding |
| 📊 Dimensional risk breakdown | Scoring engine | Safety, weather, sanctions, maintenance |
| 💡 AI-generated recommendations | Scoring engine | Grounding, compliance review, delay guidance |

Why use Aviation Safety Risk MCP Server?

Manual aviation safety research is expensive and slow. A compliance analyst checking airworthiness directives for a single aircraft type, cross-referencing OFAC for an operator, and pulling NOAA weather for a transatlantic route takes 3-4 hours per query — and involves logging into at least five separate government portals. For MRO firms tracking AD compliance across a 200-aircraft fleet, or insurers underwriting 20 carriers annually, that overhead adds up to weeks of research time.

This MCP server automates the entire data collection and scoring pipeline. Your AI agent calls a single tool, and within 60-120 seconds receives a structured JSON report with dimensional scores, risk signals, and plain-language recommendations — ready for a policy decision, audit report, or compliance workflow.

- Scheduling — run recurring fleet risk reports daily, weekly, or on a custom interval to maintain current risk profiles
- API access — call tools directly from Python, JavaScript, or any HTTP client without a separate MCP client
- Parallel data fetching — all 8 data sources are queried simultaneously, not sequentially, minimizing response time
- Monitoring — receive Slack or email alerts via Apify webhooks when airworthiness directives or sanctions matches appear
- Integrations — connect results to Zapier, Make, Google Sheets, or your aviation SMS platform via webhooks

Features

- Composite Aviation Safety Score (0-100) calculated from four weighted dimensions: airline safety defects (30%), maintenance compliance (30%), sanctions exposure (20%), and route weather risk (20%)
- Five-tier verdict system — LOW_RISK, ACCEPTABLE, ELEVATED, HIGH_RISK, CRITICAL — mapped to operational action thresholds
- Airworthiness directive detection — identifies emergency ADs, mandatory inspections, repetitive inspection requirements, and unsafe condition notices from Federal Register text using 7 keyword patterns
- Safety-critical defect classification — 17 SAFETY_CRITICAL keywords (fire, fuel, engine, hydraulic, structural, fatigue, crack, corrosion, wiring, landing gear, rudder, elevator, aileron, wing, fuselage, pressure, decompression) classify defects from safety databases
- Aviation weather threat classification — 14 AVIATION_THREATS keywords (thunderstorm, turbulence, wind shear, icing, fog, visibility, volcanic ash, hurricane, tropical storm, tornado, hail, freezing rain, blizzard, crosswind) filter NOAA alerts to aviation-relevant events
- OFAC SDN scoring with match confidence — SDN matches with 80%+ confidence score trigger BLOCKED status; 60%+ triggers HIGH risk
- OpenSanctions multi-list weighting — entities appearing on 2+ sanctions programs receive double scoring weight
- Corporate jurisdiction risk flagging — 9 high-risk jurisdictions (Iran, North Korea, Syria, Cuba, Crimea, Russia, Myanmar, Belarus, DPRK) flag corporate ownership risks from OpenCorporates data
- Correlated pattern detection — the scoring engine identifies correlated risk patterns (e.g., critical defects + emergency ADs together score 10 additional points vs. either alone)
- Plain-English signal generation — every score increment generates a specific human-readable signal string (e.g., "3 critical safety defects — fire/structural/engine concerns")
- Fleet-level and operator-level tools — fleet_risk_report covers an entire operator; individual tools allow targeted, cost-efficient queries
- Eight parallel data sources — all actor calls run via Promise.all() with 120-second timeouts and graceful fallback on individual source failures
- Spending limit enforcement — every tool checks Actor.charge() before executing and returns a structured error if the run budget is reached

Use cases for aviation safety intelligence

Aviation insurance underwriting

Insurers and Lloyd's syndicates underwriting airline hull and liability policies need fast, auditable risk scores. Use airline_safety_assessment or fleet_risk_report to generate a composite Safety Score for each applicant carrier. The structured output — safety defects, ADs, sanctions exposure, weather patterns — feeds directly into underwriting models and supports premium-setting decisions without weeks of manual research.

MRO provider compliance monitoring

Maintenance, Repair, and Overhaul providers must track active airworthiness directives for every aircraft type in their maintenance program. Use airworthiness_directive_tracker and maintenance_compliance_check to pull current ADs from the Federal Register by aircraft model, identify emergency directives requiring immediate action, and flag mandatory repetitive inspection schedules. Automate this daily so your planning team has current compliance status without manually querying the Federal Register.

Aircraft lessor due diligence

Before executing a lease agreement, lessors must screen the prospective lessee for sanctions exposure and assess their safety record. Use aircraft_sanctions_screen to check the operator against OFAC, OpenSanctions, and corporate registries, then follow with airline_safety_assessment for a full risk profile. The OpenCorporates data reveals ownership chains and identifies dissolved or high-risk-jurisdiction entities that may not appear on sanctions lists.

Route planning and operational risk management

Dispatchers and flight operations teams planning transatlantic, transpacific, or polar routes need current weather risk data before release. Use route_weather_risk with a route string (e.g., "JFK to LHR" or "North Atlantic") to pull active NOAA alerts for icing, turbulence, wind shear, and volcanic ash, plus FEMA disaster data for regional infrastructure impacts. The MARGINAL / IFR / GROUNDED verdict maps directly to flight release decision criteria.

Regulatory oversight and fleet benchmarking

Aviation authorities, industry analysts, and safety consultants benchmarking carrier performance can use compare_airlines to generate safety, maintenance, and sanctions scores for multiple operators and rank them against each other. Use incident_pattern_analysis to identify whether a carrier's defect or AD patterns are trending worse over time, flagging carriers for deeper investigation.

Aviation security and export control compliance

Companies selling aircraft parts, avionics, or maintenance services internationally must screen buyers and end-users for sanctions compliance. Use aircraft_sanctions_screen to automate OFAC and OpenSanctions screening against every new counterparty. The multi-list OpenSanctions coverage (100+ programs) catches entities that appear on EU, UN, or UK sanctions lists but not the US OFAC list.

How to use Aviation Safety Risk MCP Server

1. Connect the MCP server to your AI client — Add the server URL https://aviation-safety-risk-mcp.apify.actor/mcp to your Claude Desktop, Cursor, or Windsurf MCP configuration. Authentication uses your Apify API token in the Authorization header.
2. Ask your AI agent a safety question — Type a natural language query like "Assess the safety risk for Delta Air Lines on transatlantic routes" or "Check for airworthiness directives on the Boeing 737 MAX". The agent selects the appropriate tool automatically.
3. The server fetches and scores data in parallel — Depending on the tool, between 1 and 8 government data sources are queried simultaneously. Most tools return results in 30-90 seconds.
4. Review the structured JSON report — The agent presents the composite score, dimensional breakdowns, specific risk signals, and recommendations. Download or log the raw JSON for audit trails or CRM integration.

MCP tools

| Tool | Price | Parameters | Description |
|---|---|---|---|
| airline_safety_assessment | $0.045 | entity, route (opt) | Complete aviation safety assessment across all 8 sources. Returns composite Safety Score with full dimensional breakdown. |
| maintenance_compliance_check | $0.045 | aircraft | Maintenance compliance audit: ADs, safety bulletins, mandatory inspections from Federal Register and safety databases. |
| route_weather_risk | $0.045 | route | Route weather risk: thunderstorms, turbulence, icing, volcanic ash, and visibility from NOAA and FEMA. |
| aircraft_sanctions_screen | $0.045 | entity | Screen airline or operator against OFAC SDN, OpenSanctions (100+ lists), and corporate registries. |
| airworthiness_directive_tracker | $0.045 | aircraft, keyword (opt) | Track Federal Register ADs: emergency directives, mandatory inspections, unsafe condition notices. |
| incident_pattern_analysis | $0.045 | entity | Safety defect trends, recall patterns, and regulatory action frequency with scoring and signals. |
| compare_airlines | $0.045 | airline | Safety score, maintenance compliance, and sanctions exposure for a single airline — used for benchmarking. |
| fleet_risk_report | $0.045 | operator, region (opt) | Full fleet risk report using all 8 data sources. Composite Aviation Risk Score (0-100) for operator-level assessment. |

Tool parameter details

| Parameter | Tool(s) | Type | Required | Description |
|---|---|---|---|---|
| entity | airline_safety_assessment, aircraft_sanctions_screen, incident_pattern_analysis | string | Yes | Airline, aircraft manufacturer, or operator name |
| route | airline_safety_assessment, route_weather_risk | string | Opt / Yes | Route, airport code, or region (e.g., "JFK to LAX", "North Atlantic") |
| aircraft | maintenance_compliance_check, airworthiness_directive_tracker | string | Yes | Aircraft type, model, or manufacturer (e.g., "Boeing 737", "Airbus A320") |
| keyword | airworthiness_directive_tracker | string | No | Specific component or system to narrow AD search (e.g., "fuel system", "landing gear") |
| airline | compare_airlines | string | Yes | Airline name for comparison data |
| operator | fleet_risk_report | string | Yes | Airline operator or fleet owner name |
| region | fleet_risk_report | string | No | Operating region for weather and disaster context |

Connection examples

Claude Desktop (claude_desktop_config.json):

{

  "mcpServers": {

    "aviation-safety-risk": {

      "url": "https://aviation-safety-risk-mcp.apify.actor/mcp",

      "headers": {

        "Authorization": "Bearer YOUR_APIFY_TOKEN"

      }

    }

  }

}

Cursor / Windsurf (.cursor/mcp.json or equivalent):

{

  "aviation-safety-risk": {

    "url": "https://aviation-safety-risk-mcp.apify.actor/mcp",

    "headers": {

      "Authorization": "Bearer YOUR_APIFY_TOKEN"

    }

  }

}

Output example

Tool: airline_safety_assessment for "Emirates" on "Dubai to New York"

{
  "entity": "Emirates",
  "compositeScore": 22,
  "verdict": "ACCEPTABLE",
  "airlineSafety": {
    "score": 11,
    "safetyRecalls": 2,
    "criticalDefects": 0,
    "regulatoryActions": 1,
    "riskLevel": "GOOD",
    "signals": []
  },
  "routeWeather": {
    "score": 30,
    "severeAlerts": 2,
    "disasterCount": 1,
    "flightImpact": 14,
    "riskLevel": "VFR",
    "signals": [
      "2 aviation weather alerts along route"
    ]
  },
  "sanctionsExposure": {
    "score": 0,
    "ofacHits": 0,
    "opensanHits": 0,
    "corporateFlags": 0,
    "riskLevel": "CLEAR",
    "signals": []
  },
  "maintenanceCompliance": {
    "score": 18,
    "adCount": 3,
    "safetyBulletins": 2,
    "complianceLevel": "COMPLIANT",
    "signals": []
  },
  "allSignals": [
    "2 aviation weather alerts along route"
  ],
  "recommendations": []
}

Tool: airworthiness_directive_tracker for "Boeing 737 MAX" with keyword "fuel system"

{
  "aircraft": "Boeing 737 MAX",
  "adSummary": {
    "total": 7,
    "emergency": 1,
    "results": 12
  },
  "directives": [
    {
      "title": "Airworthiness Directives; The Boeing Company Model 737-8 and 737-9 Airplanes",
      "abstract": "This emergency AD requires replacement of the fuel shutoff valve assembly on affected aircraft...",
      "documentNumber": "2024-04-01",
      "publicationDate": "2024-02-15",
      "agencyName": "Federal Aviation Administration"
    }
  ]
}

Output fields

| Field | Type | Description |
|---|---|---|
| entity | string | The airline, operator, or aircraft queried |
| compositeScore | number | Overall Aviation Safety Score 0-100 (higher = more risk) |
| verdict | string | LOW_RISK / ACCEPTABLE / ELEVATED / HIGH_RISK / CRITICAL |
| airlineSafety.score | number | Safety defect sub-score 0-100 |
| airlineSafety.safetyRecalls | number | Total safety recall records found |
| airlineSafety.criticalDefects | number | Defects matching 17 safety-critical keywords |
| airlineSafety.regulatoryActions | number | Federal Register airworthiness actions found |
| airlineSafety.riskLevel | string | EXCELLENT / GOOD / WATCH / CONCERN / CRITICAL |
| routeWeather.score | number | Weather risk sub-score 0-100 |
| routeWeather.severeAlerts | number | NOAA alerts matching 14 aviation threat keywords |
| routeWeather.disasterCount | number | FEMA disaster declarations for the route region |
| routeWeather.flightImpact | number | Estimated operational impact score 0-30 |
| routeWeather.riskLevel | string | CLEAR / VFR / MARGINAL / IFR / GROUNDED |
| sanctionsExposure.score | number | Sanctions risk sub-score 0-100 |
| sanctionsExposure.ofacHits | number | OFAC matches at 60%+ confidence |
| sanctionsExposure.opensanHits | number | Total OpenSanctions records found |
| sanctionsExposure.corporateFlags | number | Corporate risk flags (high-risk jurisdiction or dissolved) |
| sanctionsExposure.riskLevel | string | CLEAR / LOW / MEDIUM / HIGH / BLOCKED |
| maintenanceCompliance.score | number | Maintenance compliance sub-score 0-100 |
| maintenanceCompliance.adCount | number | Airworthiness directives found |
| maintenanceCompliance.safetyBulletins | number | Safety service bulletins requiring corrective action |
| maintenanceCompliance.complianceLevel | string | EXEMPLARY / COMPLIANT / GAPS_FOUND / NON_COMPLIANT / CRITICAL |
| allSignals | string[] | Aggregated plain-English risk signals from all dimensions |
| recommendations | string[] | Actionable recommendations (grounding, compliance audit, delay) |

How much does it cost to run aviation safety assessments?

Aviation Safety Risk MCP Server uses pay-per-event pricing — you pay $0.045 per tool call. Apify platform compute costs are included. There are no subscription fees, no monthly minimums, and no per-seat charges.

| Scenario | Tool calls | Cost per call | Total cost |
|---|---|---|---|
| Quick test (single route weather check) | 1 | $0.045 | $0.045 |
| Carrier pre-screening (sanctions + safety) | 5 | $0.045 | $0.225 |
| Weekly fleet monitoring (10 operators) | 10 | $0.045 | $0.450 |
| Monthly underwriting batch (50 carriers) | 50 | $0.045 | $2.25 |
| Annual compliance program (500 queries) | 500 | $0.045 | $22.50 |

You can set a maximum spending limit per run to control costs. The server stops and returns a structured error message when your budget is reached — no surprise overages.

Apify's free tier includes $5 of monthly credits, covering approximately 111 tool calls at no cost. Compare this to commercial aviation safety data subscriptions at $500-2,000/month — most users of this server spend under $5/month for targeted queries with no subscription commitment.

Using Aviation Safety Risk MCP via the API

Python

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

    print(f"Entity: {item.get('entity')} | Score: {item.get('compositeScore')} | Verdict: {item.get('verdict')}")

Direct HTTP tool call (Python):

import requests

response = requests.post(

    "https://aviation-safety-risk-mcp.apify.actor/mcp",

    headers={

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

        "Authorization": "Bearer YOUR_APIFY_TOKEN"

    },

    json={

        "jsonrpc": "2.0",

        "method": "tools/call",

        "params": {

            "name": "airline_safety_assessment",

            "arguments": {

                "entity": "Frontier Airlines",

                "route": "Denver to Miami"

            }

        },

        "id": 1

    }

)

result = response.json()

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

print(report)

JavaScript

import { ApifyClient } from "apify-client";
const client = new ApifyClient({ token: "YOUR_API_TOKEN" });
const run = await client.actor("ryanclinton/aviation-safety-risk-mcp").call({});

const { items } = await client.dataset(run.defaultDatasetId).listItems();
for (const item of items) {

    console.log(Entity: ${item.entity} | Score: ${item.compositeScore} | Verdict: ${item.verdict});

}

Direct HTTP tool call (JavaScript):

const response = await fetch("https://aviation-safety-risk-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: "route_weather_risk",

            arguments: { route: "JFK to LHR" }

        },

        id: 1

    })

});

const data = await response.json();

console.log(data.result.content[0].text);

cURL

```bash