Litigation Intelligence MCP Server

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Quick Start

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

{
  "mcpServers": {
    "litigation-intelligence-mcp": {
      "url": "https://ryanclinton--litigation-intelligence-mcp.apify.actor/mcp"
    }
  }
}

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Litigation intelligence MCP server that detects lawsuit risk before cases are filed. This server gives any MCP-compatible AI client — Claude, Cursor, Windsurf, Cline — direct access to pre-litigation signals drawn from 7 US government data sources, scored by 4 independent risk models into a composite Litigation Probability Score (0-100).

General counsel, compliance officers, and litigation funders can ask their AI assistant to assess a company's litigation risk in natural language. The server handles all data gathering and scoring behind the scenes, returning structured risk assessments with specific action items in seconds.

What data can you access?

| Data Point | Source | Example |
|------------|--------|---------|
| 📋 Consumer complaints | CFPB Complaint Database | "Charged fees not authorized by contract" — 47 complaints |
| 📈 Complaint trend | CFPB temporal clustering | SURGING — 3.2x increase over prior quarter |
| 🏭 Environmental violations | EPA ECHO | 5 NON-COMPLIANCE records — Clean Water Act |
| 📄 SEC regulatory filings | EDGAR | 10-K, 10-Q, 8-K risk disclosures |
| ⚖️ Federal enforcement actions | Federal Register | 3 enforcement rules mentioning "penalty" |
| 🏛️ Congressional legislation | Congress.gov | 8 bills in committee; 1 enacted |
| 🚫 Sanctions exposure | OFAC SDN List | CLEAR — no SDN matches |
| 🔬 Patent portfolio | USPTO | 34 competing patents; top assignee: AlphaTech Corp |
| 📊 Composite risk score | 4-model algorithm | 72 / 100 — HIGH |
| 🚨 Class action warning | Issue concentration model | WARNING — "Billing dispute" 34% concentration |
| 📉 Enforcement trajectory | Multi-agency model | ESCALATING — EPA + Federal Register cross-signals |
| 🗂️ Legislative exposure | Bill stage classifier | SIGNIFICANT — 1 enacted, 3 advanced |

Why use Litigation Intelligence MCP?

Building a pre-litigation risk picture manually means pulling CFPB complaint exports, searching EDGAR, checking EPA ECHO facility records, reading Federal Register PDFs, scanning OFAC lists, and cross-referencing patent databases — all for a single company. A thorough review takes an analyst 4-6 hours and produces a snapshot that is stale within days.

This MCP server automates the entire process. Ask your AI assistant one question. Get back quantified scores, flagged signals, and specific action items across all seven data sources within 30-60 seconds.

- Scheduling — connect the underlying Apify actors on a recurring schedule to track risk trajectories over time
- API access — call any tool programmatically from Python, JavaScript, or any HTTP client
- Parallel execution — all 7 data sources are queried simultaneously, not sequentially
- Monitoring — get Slack or email alerts when litigation scores cross defined thresholds via Apify webhooks
- Integrations — connect outputs to Zapier, Make, Google Sheets, or directly to your GRC platform

MCP Tools

| Tool | Price | Description |
|------|-------|-------------|
| assess_litigation_risk | $0.045 | Assess litigation probability combining CFPB complaints (complaint volume + trend + disputed responses), EPA violations, and SEC filings into a Litigation Probability Score (0-100). |
| detect_class_action_signals | $0.045 | Detect class action early warning signals from CFPB complaint clustering. Measures issue concentration, product patterns, and temporal spikes. Returns NONE / WATCH / WARNING / IMMINENT. |
| track_enforcement_trends | $0.045 | Track regulatory enforcement trajectory across EPA violations, Federal Register enforcement documents, and OFAC sanctions. Returns Enforcement Trajectory Score and DECLINING / STABLE / ESCALATING / CRITICAL direction. |
| analyze_legislative_exposure | $0.045 | Analyze exposure from pending and enacted bills plus Federal Register proposed rules. Bills classified by stage: introduced, committee, passed one chamber, passed both, enacted. |
| screen_sanctions_liability | $0.045 | Screen a company or individual against the OFAC SDN list. Returns match count and CLEAR / HIGH / CRITICAL risk level. |
| monitor_patent_disputes | $0.045 | Analyze the patent landscape around a technology or company. Returns total competing patents, top assignees by patent count, and specific patent records. |
| generate_legal_landscape_report | $0.045 | Comprehensive report using all 7 data sources and 4 scoring models. Produces composite Litigation Risk Score, sub-scores for all dimensions, all signals, and prioritized action items. |

Use cases for litigation intelligence MCP

General counsel early warning

General counsel teams at mid-to-large companies run quarterly litigation risk sweeps across their top counterparties and regulated business lines. Complaint velocity on a specific product, combined with Federal Register enforcement activity, surfaces risk that would otherwise sit in siloed data. This MCP gives legal teams a structured, repeatable process with quantified metrics for board-level risk reporting.

Litigation funding assessment

Litigation funders evaluate case viability before committing capital. Complaint clustering patterns and EPA enforcement trajectories serve as upstream signals of viable litigation — high complaint concentration around a single issue is statistically correlated with subsequent class action filings. This MCP turns that signal extraction from a week-long research exercise into a 60-second query.

Insurance defense and reserve setting

Claims adjusters and actuarial teams model litigation reserves based on complaint trends and enforcement histories. A company showing a SURGING complaint trend with 5+ EPA violations and disputed complaint responses warrants higher reserves than one with STABLE trends and CLEAR enforcement records. Each scoring dimension maps directly to actuarial risk factors.

Corporate compliance risk reporting

Compliance officers preparing board-ready risk reports need quantified scores, not qualitative summaries. This MCP produces numerical scores (0-100) for each risk dimension — litigation probability, class action warning, enforcement trajectory, legislative exposure — alongside the specific signals that drove the score. That structured output feeds directly into compliance dashboards.

Patent landscape and IP litigation monitoring

Technology companies and patent counsel need to track competing patent portfolios before launching products. The monitor_patent_disputes tool queries the USPTO database, groups results by assignee, and surfaces concentration — a single entity holding 20+ patents in your technology space is a material IP litigation risk factor.

Sanctions compliance screening

Financial institutions, legal teams, and compliance departments conducting counterparty due diligence need fast OFAC SDN list verification. The screen_sanctions_liability tool returns a CLEAR / HIGH / CRITICAL classification with match counts and specific match records, directly inside any AI assistant workflow.

How to connect this litigation intelligence MCP server

Step 1 — Get your Apify API token. Go to Apify Console and copy your token from Settings > Integrations. New accounts receive $5 in free monthly credits.

Step 2 — Add the server to your MCP client. The server URL is always https://litigation-intelligence-mcp.apify.actor/mcp. Add your token as a Bearer header or as a URL parameter depending on your client.

Step 3 — Ask your AI assistant. Type a natural-language question: "What is the litigation risk for Acme Financial Services?". The AI calls the right tool automatically.

Step 4 — Review scored results. The response includes numeric scores for each dimension, specific signals that drove the score, and action items prioritized by severity.

Claude Desktop

Add to your claude_desktop_config.json:

{
  "mcpServers": {
    "litigation-intelligence": {
      "url": "https://litigation-intelligence-mcp.apify.actor/mcp",
      "headers": {
        "Authorization": "Bearer YOUR_APIFY_TOKEN"
      }
    }
  }
}

Cursor / Windsurf / Cline

Point your MCP client to:

https://litigation-intelligence-mcp.apify.actor/mcp

All MCP-compatible clients that support StreamableHTTP transport work with this server.

Token as URL parameter (some clients)

https://litigation-intelligence-mcp.apify.actor/mcp?token=YOUR_APIFY_TOKEN

Input parameters

Each tool accepts its own parameter set. All parameters are passed at call time — there is no actor-level input configuration for an MCP server.

| Tool | Parameter | Type | Required | Description |
|------|-----------|------|----------|-------------|
| assess_litigation_risk | company | string | Yes | Company name to assess |
| assess_litigation_risk | product | string | No | Product or service to narrow the complaint search |
| detect_class_action_signals | company | string | Yes | Company name |
| detect_class_action_signals | product | string | No | Product or service to narrow the complaint search |
| track_enforcement_trends | company | string | Yes | Company name |
| track_enforcement_trends | industry | string | No | Industry sector for Federal Register context |
| analyze_legislative_exposure | query | string | Yes | Industry, topic, or regulatory area |
| analyze_legislative_exposure | company | string | No | Company name for additional context |
| screen_sanctions_liability | query | string | Yes | Company name, individual, or entity to screen |
| monitor_patent_disputes | query | string | Yes | Technology, product, or company name |
| monitor_patent_disputes | assignee | string | No | Specific patent assignee to focus on |
| generate_legal_landscape_report | company | string | Yes | Company name |
| generate_legal_landscape_report | industry | string | No | Industry sector for Federal Register and Congress searches |
| generate_legal_landscape_report | product | string | No | Product or service context |

Example tool calls

Single-dimension litigation probability assessment:

{

  "tool": "assess_litigation_risk",

  "arguments": {

    "company": "Pinnacle Financial Services",

    "product": "personal loans"

  }

}

Full legal landscape report with industry context:

{

  "tool": "generate_legal_landscape_report",

  "arguments": {

    "company": "Acme Chemicals Inc",

    "industry": "chemical manufacturing",

    "product": "industrial solvents"

  }

}

Class action early warning for a specific company:

{

  "tool": "detect_class_action_signals",

  "arguments": {

    "company": "Westbridge Insurance Group"

  }

}

Input tips

- Include industry context in generate_legal_landscape_report — the industry parameter sharpens Federal Register and Congress bill searches, which use keyword matching against broad regulatory databases.
- Use product to narrow financial services assessments — CFPB complaints are categorized by product; adding "credit card" or "mortgage" reduces noise significantly.
- Combine targeted tools before running the full report — use detect_class_action_signals first to gauge whether a deep report is warranted; each tool call is independently priced.
- For sanctions screening, use the legal entity name — OFAC SDN matches against registered entity names. Use the full legal name, not trade names or abbreviations.
- For patent monitoring, use technology keywords rather than company names — patent searches return more relevant results when querying the technology domain (e.g., "neural network inference chip") rather than just the company name.

Output example

The generate_legal_landscape_report tool returns a full structured JSON report:

{
  "company": "Pinnacle Financial Services",
  "compositeScore": 72,
  "riskLevel": "HIGH",
  "litigationProbability": {
    "score": 78,
    "complaintCount": 34,
    "complaintTrend": "SURGING",
    "enforcementActions": 2,
    "edgarFilings": 5,
    "riskLevel": "HIGH",
    "signals": [
      "34 CFPB complaints — elevated litigation risk",
      "Complaint volume surging — 2x+ increase over prior quarter",
      "7 disputed/untimely complaint responses"
    ]
  },
  "classActionWarning": {
    "score": 64,
    "clusterCount": 3,
    "largestCluster": {
      "issue": "Incorrect information on your credit report",
      "count": 12
    },
    "productClusters": [
      { "product": "Credit reporting, credit repair services", "count": 18 },
      { "product": "Debt collection", "count": 9 }
    ],
    "issueClusters": [
      { "issue": "Incorrect information on your credit report", "count": 12 },
      { "issue": "Attempts to collect debt not owed", "count": 8 },
      { "issue": "Communication tactics", "count": 5 }
    ],
    "warningLevel": "WARNING",
    "signals": [
      "\"Incorrect information on your credit report\" — 12 complaints (35% concentration)",
      "3 distinct issue clusters with 5+ complaints each"
    ]
  },
  "enforcementTrajectory": {
    "score": 45,
    "epaViolations": 0,
    "federalRegisterActions": 4,
    "sanctionsExposure": 0,
    "trajectoryDirection": "STABLE",
    "agencies": ["Federal Register"],
    "signals": [
      "4 relevant Federal Register enforcement/rule documents"
    ]
  },
  "legislativeExposure": {
    "score": 55,
    "relevantBills": 9,
    "billsByStage": {
      "introduced": 4,
      "committee": 3,
      "passed_one": 1,
      "passed_both": 0,
      "enacted": 1
    },
    "highImpactBills": 2,
    "exposureLevel": "SIGNIFICANT",
    "signals": [
      "1 enacted bill(s) creating new compliance obligations",
      "1 bill(s) passed one chamber — advancing through Congress",
      "9 relevant bills in Congress — significant legislative attention"
    ]
  },
  "allSignals": [
    "34 CFPB complaints — elevated litigation risk",
    "Complaint volume surging — 2x+ increase over prior quarter",
    "7 disputed/untimely complaint responses",
    "\"Incorrect information on your credit report\" — 12 complaints (35% concentration)",
    "3 distinct issue clusters with 5+ complaints each",
    "4 relevant Federal Register enforcement/rule documents",
    "1 enacted bill(s) creating new compliance obligations",
    "1 bill(s) passed one chamber — advancing through Congress",
    "9 relevant bills in Congress — significant legislative attention"
  ],
  "actionItems": [
    "Complaint volume surging — consider proactive consumer outreach/settlement",
    "New legislation enacted — review compliance program for gaps"
  ]
}

Output fields

| Field | Type | Description |
|-------|------|-------------|
| company | string | Company name from input |
| compositeScore | number | 0-100 composite risk score (Litigation 30% + Enforcement 25% + Class Action 25% + Legislative 20%) |
| riskLevel | string | LOW / MODERATE / HIGH / CRITICAL based on composite score |
| litigationProbability.score | number | 0-100 litigation probability score |
| litigationProbability.complaintCount | number | Total CFPB complaints found |
| litigationProbability.complaintTrend | string | DECLINING / STABLE / RISING / SURGING (recent 3 months vs prior 3 months) |
| litigationProbability.enforcementActions | number | EPA violations/enforcement actions count |
| litigationProbability.edgarFilings | number | SEC 10-K, 10-Q, 8-K filings found |
| litigationProbability.riskLevel | string | LOW / MODERATE / HIGH / CRITICAL |
| litigationProbability.signals | string[] | Specific text signals that contributed to the score |
| classActionWarning.score | number | 0-100 class action risk score |
| classActionWarning.clusterCount | number | Number of distinct issue clusters with 5+ complaints |
| classActionWarning.largestCluster | object | { issue: string, count: number } — top complaint issue |
| classActionWarning.productClusters | array | Top 10 products by complaint count |
| classActionWarning.issueClusters | array | Top 10 issues by complaint count |
| classActionWarning.warningLevel | string | NONE / WATCH / WARNING / IMMINENT |
| classActionWarning.signals | string[] | Specific clustering signals |
| enforcementTrajectory.score | number | 0-100 enforcement pressure score |
| enforcementTrajectory.epaViolations | number | EPA VIOLATION / NON-COMPLIANCE records found |
| enforcementTrajectory.federalRegisterActions | number | Enforcement/penalty/rule documents in Federal Register |
| enforcementTrajectory.sanctionsExposure | number | OFAC SDN list match count |
| enforcementTrajectory.trajectoryDirection | string | DECLINING / STABLE / ESCALATING / CRITICAL |
| enforcementTrajectory.agencies | string[] | Agencies contributing to the enforcement score |
| enforcementTrajectory.signals | string[] | Specific enforcement signals |
| legislativeExposure.score | number | 0-100 legislative exposure score |
| legislativeExposure.relevantBills | number | Total relevant bills found in Congress |
| legislativeExposure.billsByStage | object | Counts by stage: introduced, committee, passed_one, passed_both, enacted |
| legislativeExposure.highImpactBills | number | Bills that have passed at least one chamber or been enacted |
| legislativeExposure.exposureLevel | string | MINIMAL / MODERATE / SIGNIFICANT / SEVERE |
| legislativeExposure.signals | string[] | Specific legislative signals |
| allSignals | string[] | All signals from all four models combined |
| actionItems | string[] | Prioritized action items generated from critical-level signals |

Scoring models

The composite Litigation Probability Score combines four independent models:

| Score Range | Risk Level | Interpretation |
|-------------|------------|----------------|
| 0-24 | LOW | Minimal litigation indicators across all data sources |
| 25-49 | MODERATE | Some complaint or enforcement activity — monitor quarterly |
| 50-74 | HIGH | Multiple active risk signals — engage legal counsel for review |
| 75-100 | CRITICAL | Strong multi-signal indicators — immediate legal assessment required |

Litigation Probability (30% weight): Derived from CFPB complaint volume (max 35 points), complaint trend acceleration (max 15 points), EPA enforcement actions (max 20 points), SEC filing presence (max 15 points), and disputed complaint responses (max 15 points). Trend acceleration is computed by comparing the mean complaint rate across the three most recent months to the three prior months. A ratio of 2.0 or above triggers the SURGING designation.

Class Action Warning (25% weight): Issue concentration score — the ratio of complaints about the single top issue to total complaints — is weighted up to 40 points. Cluster size adds up to 30 points, multiple large clusters up to 15 points, and total volume amplification up to 15 points. A top-issue concentration ratio of 30% or higher with at least 10 absolute complaints triggers a named cluster signal.

Enforcement Trajectory (25% weight): EPA VIOLATION and NON-COMPLIANCE facility records (max 30 points), Federal Register enforcement/rule/penalty documents (max 25 points), OFAC SDN matches (max 30 points, weighted heavily at 15 points each), and cross-agency amplification when multiple agencies contribute (max 15 points). A single OFAC match triggers a CRITICAL alert independently.

Legislative Exposure (20% weight): Enacted bills score 15 points each (max 35 total), bills that have passed both or one chamber score 10 and 5 points respectively, committee-stage bills add up to 20 points, total bill volume up to 20 points, and Federal Register proposed/final rules up to 25 points.

How much does it cost to run litigation intelligence queries?

Each tool call costs $0.045 regardless of how many data sources it queries internally. The generate_legal_landscape_report tool queries all 7 sources in parallel for the same $0.045 price.

| Scenario | Tool calls | Cost per call | Total cost |
|----------|------------|---------------|------------|
| Quick sanctions screen | 1 | $0.045 | $0.045 |
| Litigation probability check | 1 | $0.045 | $0.045 |
| Full legal landscape report | 1 | $0.045 | $0.045 |
| Monthly monitoring of 10 companies | 10 | $0.045 | $0.45 |
| Quarterly portfolio review of 50 companies | 50 | $0.045 | $2.25 |

You can set a maximum spending limit per run on the Apify platform to control costs. The server stops processing when your budget is reached.

Apify's free tier includes $5 of monthly platform credits — approximately 111 tool calls per month at no cost. Compare this to legal data platforms like Dun & Bradstreet Risk Analytics or LexisNexis Risk Solutions that charge $500-2,000/month for institutional subscriptions. Most users spend under $5/month.

How to use litigation intelligence with the API

Python

```python
from apify_client import ApifyClient

client = ApifyClient("YOUR_API_TOKEN")