Aviation Safety Risk MCP Server

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?

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 parameter details

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

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.

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

# Call the route_weather_risk tool directly
curl -X POST "https://aviation-safety-risk-mcp.apify.actor/mcp" \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer YOUR_APIFY_TOKEN" \
  -d '{"jsonrpc":"2.0","method":"tools/call","params":{"name":"route_weather_risk","arguments":{"route":"JFK to LAX"}},"id":1}'

# Call the fleet_risk_report tool
curl -X POST "https://aviation-safety-risk-mcp.apify.actor/mcp" \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer YOUR_APIFY_TOKEN" \
  -d '{"jsonrpc":"2.0","method":"tools/call","params":{"name":"fleet_risk_report","arguments":{"operator":"Southwest Airlines","region":"Gulf Coast"}},"id":2}'

How Aviation Safety Risk MCP Server works

Parallel data collection across 8 government sources

When a tool is called, actor-client.ts dispatches simultaneous requests to up to 8 Apify actors via Promise.all(). Each actor call uses Actor.call() with a 120-second timeout and 512 MB memory allocation. Failed or unavailable sources return empty arrays — they do not block the other sources or fail the entire request. The ACTOR_MAP routes each source name to its Apify actor ID: NHTSA Vehicle Safety (Fsr9KKRWgNDOldQ2S), FEMA Disaster Search (CcPQ8Kj5WyIVZYuv5), NOAA Weather Alerts (BJTV7I8rfiNHa0PlD), OFAC Sanctions (t398ffhJgct2clv9s), OpenSanctions (6Zlkz0wjdXewfq3yK), OpenCorporates (x0a1Q4g0MLc7h15Im), Federal Register (8bZkbWKlXQrDq0ZgK), and Data.gov (1pdOsFEBvCm5RzMfM).

Four-dimensional scoring with composite aggregation

scoring.ts runs four independent scoring functions against the collected data. Each function applies keyword lists to identify relevant records, calculates a dimensional score with a maximum cap (35 for safety defects, 30 for regulatory actions, 30 for weather, 30 for sanctions), and generates plain-English signal strings when thresholds are crossed. The composite score is a weighted average: airline safety (30%) + maintenance compliance (30%) + sanctions exposure (20%) + route weather (20%). Integer rounding is applied before verdict assignment.

The correlated pattern detector in both scoreAirlineSafety and scoreMaintenanceCompliance adds bonus points when multiple risk indicators co-occur. Two or more critical defects combined with at least one emergency AD adds 10 points to the safety score. Three or more ADs combined with three or more safety bulletins adds 5 compliance points. This reflects the industry understanding that co-occurring safety signals indicate systemic rather than isolated risk.

MCP server transport and standby mode

The server uses @modelcontextprotocol/sdk v1.12.1 with StreamableHTTPServerTransport for stateless request handling — each POST to /mcp creates a new server instance and transport, processes the request, then closes. This architecture supports the Apify Standby mode defined in actor.json ("usesStandbyMode": true), where the server remains alive between calls on port ACTOR_STANDBY_PORT. In non-standby mode (e.g., a direct actor run), the server starts, logs a health-check message, and exits cleanly after 1 second. The /mcp endpoint path is declared as "webServerMcpPath": "/mcp" in actor.json for automatic discovery by MCP clients.

Tips for best results

1. Use airline_safety_assessment for initial triage. The full 8-source assessment at $0.045 costs less than 5 cents and returns enough dimensional data to decide whether deeper investigation is warranted. Run targeted tools (e.g., airworthiness_directive_tracker) only when the initial score signals a specific concern.

2. Include route context for weather accuracy. When calling airline_safety_assessment or fleet_risk_report, pass the route or region parameter. Without it, NOAA and FEMA queries default to the entity name, which often returns no weather data. A region like "North Atlantic" or "Gulf of Mexico" produces far more relevant weather alerts.

3. Use specific aircraft models with airworthiness_directive_tracker. "Boeing 737 MAX" returns better Federal Register results than "Boeing" or "737". Adding the optional keyword parameter (e.g., "rudder", "fuel system") narrows results to a specific component when investigating a known defect pattern.

4. Schedule fleet_risk_report for recurring monitoring. Use Apify Schedules to run weekly fleet assessments for key operators. At $0.045 per run, a weekly report for 10 carriers costs $0.45/week ($1.80/month) — less than a single analyst-hour for the same research.

5. Combine with aircraft_sanctions_screen before any new business relationship. Even when overall composite scores are low, sanctions exposure warrants its own check. Use the dedicated tool for new counterparty onboarding rather than relying solely on the composite assessment, which may dilute a BLOCKED sanctions signal with a low weather score.

6. Set a spending limit for automated workflows. When integrating into an automated compliance pipeline, set maxTotalChargeUsd in your Apify run configuration. This prevents runaway costs if a workflow loops unexpectedly.

7. Log allSignals and recommendations for audit trails. These fields are designed for direct inclusion in compliance reports and audit documentation. The signal strings are specific enough to cite ("3 critical safety defects — fire/structural/engine concerns") without additional analyst interpretation.

Combine with other Apify actors

Limitations

• NHTSA data is adapted for aviation pattern analysis, not a native FAA database. Safety defect and recall-pattern scoring uses NHTSA Vehicle Safety data as a proxy for aviation safety trends. For aircraft-specific airworthiness data, the Federal Register AD tracker is the authoritative source. Direct NHTSA aviation data (SDRS/SDR) is not yet covered.
• No direct FAA accident database (ASIAS/NTSB) integration. The server accesses Federal Register ADs, Data.gov datasets, and safety-pattern proxies. It does not query the NTSB accident database, FAA Aviation Safety Information Analysis and Sharing (ASIAS), or the Aviation Safety Hotline directly.
• Weather data is US-centric. NOAA coverage is strongest for US airspace and adjacent oceanic regions. For detailed international route weather (European, Asian corridors), additional sources would improve coverage.
• Sanctions screening requires human review of matches. OFAC and OpenSanctions matches are scored by confidence, but name-matching algorithms can produce false positives. Any HIGH or BLOCKED result should be reviewed by a qualified sanctions compliance officer before taking action.
• Federal Register ADs have publication lag. Emergency ADs typically appear in the Federal Register within 24-48 hours of issuance. The system is not a substitute for subscribing directly to FAA AD notification services for real-time AD tracking.
• No fleet registration data. The server cannot determine how many aircraft of a given type are operated by a specific carrier or verify current airworthiness certificate status. Fleet size and registration data from FAA aircraft registry are not included.
• Data.gov aviation dataset coverage varies. The quality and recency of Data.gov aviation datasets depends on agency update frequency. Some datasets are updated annually; others are updated in near real-time.
• Composite score weights are fixed. The 30/30/20/20 weighting (safety/maintenance/sanctions/weather) reflects general-purpose aviation risk assessment. Users with specific weighting needs (e.g., a cargo insurer weighting weather at 40%) should post-process the dimensional scores rather than relying on the composite.

Integrations

• Zapier — trigger fleet_risk_report on a schedule and push high-risk results to Slack, email, or a compliance ticket system
• Make — build automated AD monitoring workflows that check Federal Register for new airworthiness directives weekly
• Google Sheets — export fleet risk scores for all monitored operators to a live Google Sheet for management dashboards
• Apify API — call MCP tools directly from Python or JavaScript compliance pipelines without an MCP client
• Webhooks — fire alerts to your SMS, aviation safety management system, or Slack channel when a CRITICAL verdict or BLOCKED sanctions score is returned
• LangChain / LlamaIndex — expose aviation safety data as a retrieval tool in RAG pipelines for AI-assisted regulatory research and compliance Q&A

Troubleshooting

• Tool returns empty signals despite a known high-risk entity. The scoring algorithms depend on finding entity name matches in public government databases. For airlines known by multiple names (DBA vs. legal entity name), try the parent company name, the ICAO airline code spelling, or the operator's formal legal entity name. For example, "Spirit Airlines" may return different results than "Spirit Aviation Holdings".

• Route weather tool returns CLEAR for a known weather event. NOAA alert coverage depends on the search query matching the alert's geographic description. Try more specific region names ("Gulf of Mexico", "Caribbean") or airport codes ("MIA", "IAH") rather than city names. FEMA disaster coverage is strongest for declared federal disasters and may not capture local weather events.

• Sanctions screening returns no results for an entity on a known list. OpenSanctions and OFAC name matching is fuzzy but sensitive to transliteration and abbreviation. Try the entity's name in multiple variants — English spelling, local spelling, and known aliases. If the entity is known only by ICAO designator or registration, the sanctions search may not match.

• Tool times out after 120 seconds. Individual actor calls have a 120-second timeout. If one data source is slow, the timeout for that source is silent and that source returns an empty array — it does not block other sources. Full timeouts on multiple sources simultaneously can cause a partial result. Retry the request; transient API load typically resolves within a few minutes.

• Spending limit reached error mid-batch. If you see "eventChargeLimitReached": true, your run's spending limit was hit before the tool could execute. Increase maxTotalChargeUsd in your Apify run configuration or run fewer tools per session.

Responsible use

• This server only accesses publicly available data from US government databases (Federal Register, NOAA, FEMA, OFAC, Data.gov) and open corporate registries.
• Sanctions screening results must be reviewed by a qualified compliance officer before any business action is taken. This server does not constitute legal compliance advice.
• Aviation safety decisions — including grounding, route cancellation, or lease rejection — must involve qualified aviation safety professionals. This server provides decision-support intelligence, not regulatory determinations.
• Do not use screening results for discriminatory purposes against airlines based solely on national origin or ownership without substantive supporting evidence.
• For guidance on web scraping and data use legality, see Apify's guide.

FAQ

How does aviation safety risk scoring work in this MCP server? The server runs four scoring models in parallel: airline safety defects (max 35 recall-pattern points + 30 regulatory action points), maintenance compliance (AD count, emergency directives, mandatory inspections), route weather risk (NOAA aviation threat alerts + FEMA disaster data), and sanctions exposure (OFAC match confidence + OpenSanctions multi-list + corporate jurisdiction flags). A composite weighted average produces the final 0-100 score. Scores above 80 trigger CRITICAL status; 60-79 trigger HIGH_RISK.

How accurate is the airworthiness directive detection? The airworthiness_directive_tracker and maintenance_compliance_check tools search Federal Register text using 7 keyword patterns: "airworthiness directive", "ad ", "emergency ad", "unsafe condition", "mandatory inspection", "mandatory replacement", and "grounding". Any Federal Register entry matching these patterns is counted as an AD. This approach catches all published ADs but may include regulatory notices that reference ADs without issuing new ones. For definitive AD compliance status, cross-reference the Federal Register results with the FAA's official AD database.

Does this MCP server access real-time FAA safety data? Federal Register data reflects published entries, which typically appear 24-48 hours after FAA issuance for emergency ADs. NOAA weather alerts are near real-time (updated every few minutes). FEMA disaster declarations reflect officially declared emergencies. The server does not access the FAA's live Safety Data (SDRS) system, ASIAS, or NTSB accident database directly.

How is this different from Aviation Safety Network or FlightAware data? Aviation Safety Network provides historical accident and incident records. FlightAware provides live flight tracking and delay data. This MCP server provides compliance intelligence — airworthiness directives, sanctions screening, maintenance compliance, and regulatory action patterns — from government databases. It is designed for underwriters, lessors, MRO providers, and compliance teams, not for passenger-facing flight tracking.

Can I screen multiple airlines in a single run? Each tool call processes one entity per call. To screen 10 airlines, you would make 10 airline_safety_assessment calls at $0.045 each ($0.45 total). There is no batch input parameter. For bulk processing, use the Apify API with a loop in your application code, or use Apify Schedules to run sequential assessments overnight.

Is it legal to use this data for sanctions compliance screening? Yes. All data sources are publicly available US government databases. OFAC publishes its SDN list specifically for compliance screening purposes. OpenSanctions aggregates publicly released government watchlists. Using this data for regulatory compliance screening is the intended use of these public databases. See Apify's guide on web scraping legality for general context.

How long does a typical tool call take? Single-source tools (route_weather_risk, airworthiness_directive_tracker) typically return in 15-45 seconds. Multi-source tools (airline_safety_assessment, fleet_risk_report) that query all 8 sources simultaneously typically return in 45-120 seconds, depending on the response speed of each underlying actor. All sources are queried in parallel, so total time equals the slowest source, not the sum.

Can I schedule recurring aviation safety monitoring? Yes. Use Apify Schedules to trigger fleet_risk_report daily, weekly, or monthly for each operator in your portfolio. Combine with Apify Webhooks to push CRITICAL or HIGH_RISK verdicts to your safety management system or Slack channel automatically. A weekly fleet check for 10 carriers costs $0.45/week with no additional infrastructure.

What happens if an OFAC or sanctions match is found? Any OFAC SDN match with 80%+ confidence score returns riskLevel: "BLOCKED" and a compositeScore that triggers CRITICAL verdict. The raw OFAC match records are included in the response under ofacResults for manual review. The server does not block business operations — it returns structured data for human decision-making. A qualified sanctions compliance officer should review all BLOCKED or HIGH sanctions results before action.

How are corporate jurisdiction risks flagged? OpenCorporates data for the entity is checked against 9 high-risk jurisdictions: Iran, North Korea (DPRK), Syria, Cuba, Crimea, Russia, Myanmar, and Belarus. Any match adds to corporateFlags. Inactive or dissolved entities also add to the flag count. This catches beneficial ownership structures where a lessor or operator has hidden connections to sanctioned jurisdictions even if the entity itself is not on a watchlist.

Can this MCP server be used in a LangChain or LlamaIndex pipeline? Yes. Any MCP-compatible client can connect to the server. For LangChain, use an MCP tool adapter to expose the 8 tools as callable functions. For LlamaIndex, use the FunctionTool wrapper around direct HTTP calls to the /mcp endpoint. The structured JSON output is designed for direct LLM consumption — scores, signals, and recommendations are formatted for natural language summarization without additional parsing.

What is the minimum cost to try aviation safety risk scoring? A single route_weather_risk or airworthiness_directive_tracker call costs $0.045. Apify's free tier includes $5 of monthly credits, covering approximately 111 tool calls. New Apify accounts can test the server at no cost within the free tier.

Help us improve

If you encounter issues, you can help us debug faster by enabling run sharing in your Apify account:

1. Go to Account Settings > Privacy
2. Enable Share runs with public Actor creators

This lets us see your run details when something goes wrong, so we can fix issues faster. Your data is only visible to the actor developer, not publicly.

Support

Found a bug or have a feature request? Open an issue in the Issues tab on this actor's page. For custom integrations, enterprise licensing, or additional aviation data source coverage, reach out through the Apify platform.