Higher Education Risk MCP Server

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---

Quick Start

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

{
  "mcpServers": {
    "higher-education-risk-mcp": {
      "url": "https://ryanclinton--higher-education-risk-mcp.apify.actor/mcp"
    }
  }
}

---

Higher education risk intelligence for any university, college, or for-profit institution — delivered through the Model Context Protocol. This MCP server connects your AI assistant to 8 live data sources and returns structured risk assessments covering accreditation standing, research integrity, federal funding dependency, and regulatory exposure. Purpose-built for accreditors, student loan portfolio managers, state regulators, and institutional researchers who need objective institution health data without manual database searching.

The server runs in persistent standby mode on the Apify platform, accepting tool calls from Claude Desktop, Cursor, Windsurf, Cline, or any MCP-compatible client. Each request fans out to multiple data sources in parallel — CFPB complaints, Federal Register publications, OpenAlex publications, ORCID researcher profiles, USAspending awards, Grants.gov portfolios, and Wikipedia — then applies four scoring models to produce a composite risk score with plain-language signals.

What data can you extract?

| Data Point | Source | Example |
|---|---|---|
| 📊 Composite institution risk score (0-100) | 4 scoring models | compositeScore: 67 |
| 🏛️ Accreditation risk level | CFPB + Federal Register + Wikipedia | riskLevel: "AT_RISK" |
| 🔬 Research integrity index (0-100) | OpenAlex + ORCID | integrityLevel: "GOOD", score: 71 |
| 💰 Federal funding total (USD) | USAspending | federalFunding: 48200000 |
| 📋 Grant portfolio diversity | Grants.gov | fundingDiversity: 5, grantCount: 12 |
| ⚠️ Closure risk rating (1-5) | Financial viability model | closureRisk: 2 |
| 📰 Title IV regulatory threats | Federal Register | titleIVThreats: 3 |
| 🗂️ Student complaint volume | CFPB Complaint Database | complaintVolume: 47 |
| 📈 Complaint acceleration signal | CFPB trend analysis | recent: 18 vs prior: 6 |
| 🔴 Top complaint issues (clustered) | CFPB categorization | "Student loan — payment issues": 14 |
| 👩‍🔬 ORCID-registered researchers | ORCID open registry | totalResearchers: 28 |
| 📖 Research publications with citation counts | OpenAlex | totalPapers: 34, citationHealth: 22 |

Why use Higher Education Risk MCP Server?

Manual institution due diligence means switching between the CFPB complaint portal, Federal Register search, USAspending.gov, Grants.gov, OpenAlex, and ORCID — then building spreadsheets to compare results. A single institution screening can take 4-6 hours. For a portfolio of 20 schools, that is weeks of analyst time.

This MCP server automates the entire process. Call one tool, get a structured risk assessment in under 2 minutes. Run peer comparisons across 5 institutions simultaneously. Schedule quarterly monitoring without touching a spreadsheet.

- Scheduling — run institution portfolio sweeps on a quarterly or annual cadence to track risk trajectory over time
- API access — trigger assessments from Python, JavaScript, or any HTTP client for programmatic integration into risk platforms
- Parallel data collection — up to 8 data sources queried simultaneously per tool call, not sequentially
- Monitoring — get Slack or email alerts when institution assessments complete or when spending limits are approached
- Integrations — connect to Zapier, Make, Google Sheets, HubSpot, or any webhook-capable system for post-processing

Features

- Four independent scoring models — Accreditation Risk (0-100, risk-oriented), Research Integrity Index (0-100, quality-oriented), Financial Viability Score (0-100, health-oriented), and Regulatory Exposure Score (0-100, risk-oriented) computed from separate data source combinations
- Composite risk score — a single 0-100 institution health number derived as a weighted average of all four models: (100 − accreditation) × 0.25 + research × 0.25 + financial × 0.25 + (100 − regulatory) × 0.25
- Five-tier risk classification — LOW, MODERATE, ELEVATED, HIGH, and CRITICAL labels mapped to composite score bands for fast triage
- Complaint acceleration detection — compares complaint volume in the trailing 6 months against the prior period; flags when recent count exceeds 1.5× older count, signalling a deteriorating trajectory
- Title IV threat scoring — Federal Register documents are scanned for "Title IV", "student aid", "gainful employment", "borrower defense", and "accreditation" keywords, with each hit weighted toward the regulatory exposure score
- For-profit school pattern detection — specifically scores elevated risk when Wikipedia or deep research content contains both "for-profit" and "criticism" or "lawsuit"
- Citation health scoring — uses log2(avgCitations) × 5 to produce a logarithmic citation quality score, avoiding inflation from a single high-cited outlier paper
- Journal diversity scoring — counts unique publication venues across OpenAlex results; institutions publishing across 5+ journals receive a diversity bonus
- Closure risk projection (1-5 scale) — converts financial viability score to a 5-point closure risk rating aligned to common institutional oversight frameworks
- Strengths and risks narrative — the full dossier tool auto-generates plain-language strength and risk bullet points from scoring signals for direct use in reports
- Parallel actor orchestration — runActorsParallel dispatches all data source calls via Promise.allSettled, so individual source failures do not abort the entire assessment
- Spending limit guards — every tool checks Actor.charge() result and returns a clean error message if the per-run event charge limit has been reached
- 8 MCP tools covering targeted and comprehensive use cases from quick snapshots to full dossiers

Use cases for higher education risk intelligence

Accreditation agency risk-based monitoring

Regional and national accreditors carry oversight responsibility for hundreds of institutions. Manually tracking complaint trends, regulatory actions, and financial signals across a large portfolio is prohibitive. This server gives accreditation staff a structured score for every institution at review time — surfacing which schools need deeper scrutiny and which are stable — so oversight resources focus where the risk is highest.

Student loan portfolio management

Servicers and investors holding student loan portfolios need early warning when institutions show closure risk indicators. Institutional closures trigger borrower defense claims and loan discharges. The Financial Viability Model and Accreditation Risk Score provide leading indicators — declining federal funding, rising complaint volumes, and Federal Register enforcement patterns — before a closure event becomes public.

State higher education regulatory oversight

State higher education agencies monitor dozens to hundreds of institutions for consumer protection, degree integrity, and financial stability. This server's compare_peer_institutions and scan_student_complaints tools let state staff quickly identify which institutions in their jurisdiction are accumulating student complaints or regulatory flags without running separate database searches.

Pre-enrollment due diligence for prospective students

Students and families committing to multi-year enrollment and student loan obligations benefit from objective institutional health data. The assess_institution_risk tool returns a plain-language institutional snapshot — complaint volume, accreditation signals, research credentials — in seconds, supporting informed enrollment decisions.

Institutional investor and private equity due diligence

Education sector investors evaluating acquisitions of for-profit institutions need financial viability data, complaint exposure, and regulatory risk profiles alongside traditional financial due diligence. This server surfaces the public-data risk signals that inform pricing and deal structure for higher education transactions.

Academic research and policy analysis

Higher education researchers studying institutional quality, closure patterns, or regulatory effectiveness can use this server to build longitudinal datasets across hundreds of institutions. The structured JSON output integrates cleanly with Python analysis pipelines, reducing the manual data collection burden for large-scale studies.

How to use the Higher Education Risk MCP Server

Connect to Claude Desktop

Add this entry to your claude_desktop_config.json:

{
  "mcpServers": {
    "higher-education-risk": {
      "url": "https://higher-education-risk-mcp.apify.actor/mcp",
      "headers": {
        "Authorization": "Bearer YOUR_APIFY_TOKEN"
      }
    }
  }
}

Restart Claude Desktop. The 8 higher education risk tools will appear in your tools panel.

Run your first assessment

1. Name the institution — type the full institution name as it appears officially: "University of Phoenix", "DeVry University", "Western Governors University", or any accredited institution
2. Choose the right tool — use assess_institution_risk for a quick snapshot, generate_institution_dossier for comprehensive due diligence, or a specialized tool for targeted analysis
3. Wait for data collection — the server queries up to 8 data sources in parallel; most tools complete in 60-90 seconds
4. Read the structured result — the response includes a risk score, classification level, plain-language signals, and raw supporting data you can use directly in reports

MCP tools

| Tool | Sources queried | Price | What you get |
|------|----------------|-------|-------------|
| assess_institution_risk | Company Deep Research, Wikipedia, CFPB | $0.045 | Accreditation Risk Score, institutional profile, complaint summary |
| check_accreditation_exposure | Federal Register, CFPB | $0.045 | Regulatory Exposure Score, Title IV threats, enforcement count, student complaint triggers |
| analyze_research_integrity | OpenAlex, ORCID | $0.045 | Research Integrity Index 0-100, paper count, citation health, researcher count, journal diversity |
| audit_federal_funding_dependency | USAspending, Grants.gov | $0.045 | Financial Viability Score, total federal funding, grant count, funding diversity, closure risk 1-5 |
| scan_student_complaints | CFPB | $0.045 | Total complaint count, dispute rate, top 10 issue clusters, complaint acceleration signals |
| track_regulatory_actions | Federal Register | $0.045 | Rule type breakdown (proposed / final / notice), full regulation list, topic or institution-specific |
| compare_peer_institutions | OpenAlex, USAspending, CFPB | $0.045 | Side-by-side ranking of 2-5 institutions by research score, funding score, and complaint volume |
| generate_institution_dossier | All 8 data sources | $0.045 | Composite score, all 4 scoring models, strengths list, risks list, all signals, full supporting data |

Output example

The following is representative output from generate_institution_dossier for a mid-sized for-profit institution:

{
  "institution": "Pinnacle Career Institute",
  "compositeScore": 38,
  "overallRisk": "ELEVATED",
  "accreditationRisk": {
    "score": 61,
    "complaintVolume": 34,
    "regulatoryActions": 18,
    "reputationFlags": 14,
    "riskLevel": "AT_RISK",
    "signals": [
      "34 consumer complaints — elevated student dissatisfaction",
      "Multiple regulatory actions in Federal Register — accreditation under scrutiny",
      "Wikipedia/research sources flag institutional controversies or closure risk",
      "Complaint acceleration: 19 in last 6 months vs 9 prior — deteriorating trajectory"
    ]
  },
  "researchIntegrity": {
    "score": 29,
    "totalPapers": 4,
    "totalResearchers": 2,
    "citationHealth": 3,
    "integrityLevel": "BELOW_AVERAGE",
    "signals": [
      "Minimal research output — limited academic credibility",
      "No ORCID-registered researchers found — research credibility concern"
    ]
  },
  "financialViability": {
    "score": 34,
    "federalFunding": 2800000,
    "grantCount": 1,
    "fundingDiversity": 1,
    "closureRisk": 2,
    "signals": [
      "Low federal funding level — financial vulnerability",
      "No active grants found — limited competitive funding success"
    ]
  },
  "regulatoryExposure": {
    "score": 58,
    "activeRules": 11,
    "titleIVThreats": 4,
    "enforcementActions": 2,
    "exposureLevel": "HIGH",
    "signals": [
      "4 Title IV / student aid regulatory actions — federal funding at risk",
      "2 enforcement actions — active regulatory scrutiny",
      "12 student loan/education complaints — regulatory trigger risk"
    ]
  },
  "allSignals": [
    "34 consumer complaints — elevated student dissatisfaction",
    "Multiple regulatory actions in Federal Register — accreditation under scrutiny",
    "Complaint acceleration: 19 in last 6 months vs 9 prior — deteriorating trajectory",
    "Minimal research output — limited academic credibility",
    "Low federal funding level — financial vulnerability",
    "4 Title IV / student aid regulatory actions — federal funding at risk"
  ],
  "strengths": [],
  "risks": [
    "Accreditation AT_RISK — 34 complaints, 18 regulatory actions",
    "Closure risk elevated (2/5) — limited federal funding and grant activity",
    "HIGH regulatory exposure — 4 Title IV threats",
    "Poor research integrity — minimal publications and no ORCID researchers"
  ]
}

Output fields

| Field | Type | Description |
|-------|------|-------------|
| institution | string | Institution name as queried |
| compositeScore | number | 0-100 overall institution health score (higher = healthier) |
| overallRisk | string | LOW / MODERATE / ELEVATED / HIGH / CRITICAL |
| accreditationRisk.score | number | 0-100 accreditation risk (higher = more risk) |
| accreditationRisk.complaintVolume | number | Total CFPB complaints found |
| accreditationRisk.regulatoryActions | number | Weighted regulatory action count from Federal Register |
| accreditationRisk.reputationFlags | number | Reputation flag count from Wikipedia and deep research |
| accreditationRisk.riskLevel | string | STABLE / WATCH / CONCERN / AT_RISK / CRITICAL |
| accreditationRisk.signals | string[] | Plain-language risk signals |
| researchIntegrity.score | number | 0-100 research quality score (higher = better) |
| researchIntegrity.totalPapers | number | OpenAlex publication count |
| researchIntegrity.totalResearchers | number | ORCID-registered researcher count |
| researchIntegrity.citationHealth | number | Average citations per paper |
| researchIntegrity.integrityLevel | string | POOR / BELOW_AVERAGE / AVERAGE / GOOD / EXCELLENT |
| financialViability.score | number | 0-100 financial health score (higher = healthier) |
| financialViability.federalFunding | number | Total federal funding in USD from USAspending |
| financialViability.grantCount | number | Active grants from Grants.gov |
| financialViability.fundingDiversity | number | Count of distinct federal funding agencies |
| financialViability.closureRisk | number | 1 (highest closure risk) to 5 (lowest) |
| regulatoryExposure.score | number | 0-100 regulatory exposure (higher = more exposed) |
| regulatoryExposure.activeRules | number | Federal Register entries found |
| regulatoryExposure.titleIVThreats | number | Title IV / student aid documents found |
| regulatoryExposure.enforcementActions | number | Enforcement-type documents found |
| regulatoryExposure.exposureLevel | string | MINIMAL / LOW / MODERATE / HIGH / SEVERE |
| allSignals | string[] | Merged plain-language signals from all four models |
| strengths | string[] | Auto-generated strength statements from positive scoring outcomes |
| risks | string[] | Auto-generated risk statements from negative scoring outcomes |

How much does it cost to assess higher education risk?

This server uses pay-per-event pricing — you pay $0.045 per tool call. Platform compute costs are included. There is no subscription fee. The Apify Free plan includes $5 of monthly credits, which covers over 100 institution assessments.

| Scenario | Tool calls | Cost per call | Total cost |
|----------|-----------|---------------|------------|
| Quick institution check | 1 | $0.045 | $0.045 |
| Department-level research audit | 1 | $0.045 | $0.045 |
| Three-tool deep dive on one institution | 3 | $0.045 | $0.135 |
| Full dossier on 10 institutions | 10 | $0.045 | $0.45 |
| Quarterly portfolio sweep (50 institutions) | 50 | $0.045 | $2.25 |

You can set a maximum spending limit per run to control costs. The server returns a clean error message if the per-run event charge limit is reached, and the run stops gracefully.

Compare this to manual database searches across CFPB, Federal Register, USAspending, OpenAlex, and ORCID — averaging 4-6 hours per institution at analyst rates of $50-150/hour. A 50-institution portfolio sweep that costs $2.25 here would cost $10,000-30,000 in analyst time.

Using the Higher Education Risk MCP Server via the API

Python

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

    print(f"Institution: {item.get('institution')} | Risk: {item.get('overallRisk')} | Score: {item.get('compositeScore')}")

For direct MCP tool calls from Python, use the HTTP endpoint:

import requests
response = requests.post(

    "https://higher-education-risk-mcp.apify.actor/mcp",

    headers={

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

        "Authorization": "Bearer YOUR_APIFY_TOKEN"

    },

    json={

        "jsonrpc": "2.0",

        "method": "tools/call",

        "params": {

            "name": "generate_institution_dossier",

            "arguments": {"institution": "Grand Canyon University"}

        },

        "id": 1

    }

)

dossier = response.json()

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

print(result)

JavaScript

import { ApifyClient } from "apify-client";
const client = new ApifyClient({ token: "YOUR_API_TOKEN" });
// Direct MCP HTTP call

const response = await fetch("https://higher-education-risk-mcp.apify.actor/mcp", {

    method: "POST",

    headers: {

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

        "Authorization": "Bearer YOUR_API_TOKEN"

    },

    body: JSON.stringify({

        jsonrpc: "2.0",

        method: "tools/call",

        params: {

            name: "compare_peer_institutions",

            arguments: {

                institutions: [

                    "University of Phoenix",

                    "Western Governors University",

                    "Southern New Hampshire University"

                ]

            }

        },

        id: 1

    })

});
const data = await response.json();

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

for (const inst of comparison.peerComparison) {

    console.log(${inst.institution}: research=${inst.researchScore}, funding=${inst.fundingScore}, complaints=${inst.complaints});

}

cURL

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