Corporate Political Exposure MCP Server

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Quick Start

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

{
  "mcpServers": {
    "corporate-political-exposure-mcp": {
      "url": "https://ryanclinton--corporate-political-exposure-mcp.apify.actor/mcp"
    }
  }
}

---

Corporate political exposure intelligence for AI agents via the Model Context Protocol. This MCP server queries 11 federal and international data sources in parallel — Senate lobbying, FEC campaign finance, congressional stock trades, FARA foreign agent registrations, SAM.gov contracts, USAspending, OFAC sanctions, OpenSanctions, and OpenCorporates — and applies 5 proprietary scoring models to produce a composite Political Exposure Score (0-100).

This tool is designed for compliance teams, ESG analysts, investment due diligence workflows, and AI agents that need structured political risk intelligence on demand. No subscriptions, no manual research — call a tool, get a scored JSON result.

What data can you access?

| Data Point | Source | Coverage |
|------------|--------|----------|
| 📋 Lobbying registrations and expenditures | Senate Lobbying Disclosure (LD-1/LD-2) | All filers, all quarters |
| 💰 Political contributions, amounts, recipients | FEC Campaign Finance | Full FEC public database |
| 📈 Congressional member stock trades | STOCK Act Disclosures | All reported transactions |
| 🌐 Foreign agent registrations | FARA Foreign Agents | All FARA registrations |
| 🏛️ Legislative bills and sponsorships | Congress Bills | Current and recent sessions |
| 📰 Regulatory actions and rulemakings | Federal Register | Executive orders, rules, notices |
| 🏗️ Federal contract registrations | SAM.gov | All registrations |
| 💵 Federal spending and awards | USAspending | All award obligations |
| 🚨 US Treasury sanctions matches | OFAC SDN List | Specially Designated Nationals |
| 🌍 Global PEP and watchlist matches | OpenSanctions | 100+ screening programs |
| 🏢 Corporate registry and entity data | OpenCorporates | 140+ jurisdictions |

MCP tools

| Tool | Price | Data sources | What it returns |
|------|-------|-------------|-----------------|
| political_exposure_scan | $2.00 | 5 sources | Lobbying count, FEC count, congressional trades, sanctions flags — quick risk summary |
| lobbying_activity_report | $2.00 | 1 source (deep) | Top lobbying firms, top issues, filing count — full lobbying breakdown |
| campaign_finance_map | $2.00 | 1 source (deep) | Top 15 recipients, total amount, contribution counts — finance map |
| congressional_interest_check | $2.00 | 2 sources | Buy/sell trade counts, BULLISH/BEARISH/MIXED sentiment, related bills |
| foreign_influence_screen | $2.00 | 4 sources | Foreign Influence Score (0-100), FARA count, OFAC/sanctions matches, corporate entities |
| legislative_threat_assessment | $2.00 | 6 sources | Legislative Threat Score (-100 to +100), direction label, bill analysis |
| government_revenue_dependency | $2.00 | 4 sources | Political Dependency Score (0-100), contract count, total federal awards |
| influence_network_graph | $5.00 | All 11 sources | Composite Political Exposure Score (0-100), 5 dimensional scores, grade, recommendation |

Why use this MCP for corporate political risk?

Manual political due diligence requires searching the FEC website, Senate lobbying database, FARA registry, SAM.gov, USAspending, and multiple sanctions lists — each with different interfaces, download formats, and update cycles. For a single company this takes 4-8 hours. For a portfolio of 50 companies, it becomes a dedicated research project.

This MCP server dispatches all queries in parallel, normalizes data across sources, scores every dimension with documented formulas, and returns structured JSON in 30-120 seconds. Your AI agent or application gets an actionable risk score, not a pile of raw government data to interpret.

- Scheduling — run recurring political exposure monitoring on a daily, weekly, or monthly schedule via Apify
- API access — trigger from Python, JavaScript, n8n, or any HTTP client with a token
- Proxy rotation — queries run via Apify infrastructure; no IP blocks or rate limit handling on your side
- Monitoring — configure Slack or email alerts when runs fail or scores exceed thresholds
- Integrations — push results to Zapier, Make, HubSpot, or any webhook endpoint

Features

- 5 proprietary scoring models — Political Dependency (0-100), Influence Network Density (0-100), Legislative Threat/Opportunity (-100 to +100), Foreign Influence Exposure (0-100), and Revolving Door Index (0-100)
- Weighted composite scoring — Foreign influence (25%) + Political dependency (25%) + Influence network (20%) + Revolving door (15%) + Legislative threat (15%) produces a single defensible score
- 11 data sources, all parallel — the influence_network_graph tool fires all 11 actor queries simultaneously and aggregates results; no sequential bottlenecks
- Pay-per-charge billing — every tool call checks Actor.charge() first; your spending cap is enforced at the tool level, not at the run level
- Sanctions escalation logic — any OFAC or OpenSanctions match automatically elevates the Legislative Threat Score by -30 and the Foreign Influence Score by 20-30 points regardless of other signals
- Multi-channel bonus scoring — Influence Network model adds 10 points when 3 or more distinct influence channels (lobbying, campaign finance, congressional trading, foreign agents) are simultaneously active
- Congressional sentiment detection — buy/sell ratio analysis on STOCK Act trades classifies congressional sentiment as BULLISH, BEARISH, or MIXED using a 2:1 ratio threshold with a minimum 3-trade floor
- Bill direction classification — scans bill titles for 10 supportive keywords (support, promote, invest, incentive, authorize) and 6 restrictive keywords (restrict, prohibit, ban, regulate, enforce, penalty) to classify the legislative environment
- Revolving door pattern detection — high lobbying activity combined with government contract presence triggers a "contract-lobbying reinforcement loop" finding; FARA + domestic lobbying combination triggers "international revolving door" detection
- Composite grade labels — EXTREME POLITICAL EXPOSURE (70+), HIGH (50-69), MODERATE (30-49), LOW (0-29) with corresponding actionable recommendations
- Stateless MCP transport — runs in Apify Standby mode via StreamableHTTP at /mcp; each request is fully isolated with no shared session state

Use cases for corporate political exposure intelligence

ESG governance and institutional reporting

ESG governance teams at asset managers and pension funds need standardized political exposure metrics for their portfolio companies. The influence_network_graph tool delivers a scored, structured output that maps directly to the governance pillar of ESG frameworks. Run it quarterly on your holdings list and track score changes as a leading indicator of political risk shifts.

M&A political due diligence

Advisors conducting pre-close due diligence on acquisition targets need to surface hidden political dependencies and influence relationships before deal pricing. Run government_revenue_dependency to quantify federal revenue concentration, foreign_influence_screen to catch FARA and sanctions exposure, and influence_network_graph for the composite view. An elevated political exposure score is a negotiating lever and a disclosure obligation.

Investment research and congressional alpha signals

Quantitative analysts and equity researchers monitor congressional stock trading as a leading signal of legislative intent. The congressional_interest_check tool returns raw buy/sell counts alongside a BULLISH/BEARISH/MIXED sentiment classification for any company. Paired with legislative_threat_assessment, it reveals whether lawmakers are positioning ahead of favorable or restrictive legislation.

Anti-corruption and KYB compliance screening

Compliance officers performing Know Your Business (KYB) screening on counterparties, vendors, and partners need to detect foreign influence connections and PEP-adjacent relationships. The foreign_influence_screen tool hits FARA, OFAC, and OpenSanctions simultaneously, returning a Foreign Influence Exposure Score with individual findings for each source.

Government affairs strategy and monitoring

Government affairs teams at companies with regulatory exposure need to track the legislative environment on an ongoing basis. The legislative_threat_assessment tool classifies the current bill landscape as a STRONG OPPORTUNITY, MILD OPPORTUNITY, NEUTRAL, MILD THREAT, or SEVERE THREAT using real-time congressional bill data and Federal Register activity.

Competitive intelligence and lobbying benchmarking

Strategy teams at corporations and trade associations benchmark their own political influence activity against competitors. Run lobbying_activity_report and campaign_finance_map on multiple companies in a sector and compare lobbying firm relationships, issue coverage, and campaign finance totals side by side.

How to connect this MCP server

Step 1: Get your Apify API token

Sign in at console.apify.com, go to Settings > Integrations, and copy your API token.

Step 2: Add to Claude Desktop

Add to your claude_desktop_config.json:

{
  "mcpServers": {
    "corporate-political-exposure": {
      "url": "https://corporate-political-exposure-mcp.apify.actor/mcp",
      "headers": {
        "Authorization": "Bearer YOUR_APIFY_TOKEN"
      }
    }
  }
}

Step 3: Configure your AI client

This MCP server works with Claude Desktop, Cursor, Windsurf, Cline, Continue, and any other MCP-compatible client. Use the same endpoint URL and Authorization header pattern.

Step 4: Call a tool

Ask your AI assistant: "Run a political exposure scan on Raytheon Technologies" and the agent will call political_exposure_scan automatically. For a full assessment, ask for an "influence network graph" on any company.

MCP tool reference

All tools accept companyName: string. Tools marked with sector also accept an optional sector: string to broaden legislative bill searches.

| Tool | Input | Returns |
|------|-------|---------|
| political_exposure_scan | companyName | Filing counts, sanctions hits, active risk flags across 5 sources |
| lobbying_activity_report | companyName | Total filings, top 10 firms, top 10 lobbied issues |
| campaign_finance_map | companyName | Total contributions, total amount, top 15 recipients |
| congressional_interest_check | companyName, sector | Trade buy/sell counts, BULLISH/BEARISH/MIXED sentiment, related bills |
| foreign_influence_screen | companyName | Foreign Influence Score 0-100, FARA/OFAC/sanctions counts |
| legislative_threat_assessment | companyName, sector | Legislative Threat Score -100 to +100, direction label, bill analysis |
| government_revenue_dependency | companyName | Political Dependency Score 0-100, contract count, total federal awards |
| influence_network_graph | companyName, sector | Composite score, all 5 dimensional models, grade, recommendation |

Quick example — full assessment:

{ "companyName": "Lockheed Martin", "sector": "defense" }

Output example

The influence_network_graph tool returns structured JSON like this:

{
  "company": "Northrop Grumman",
  "politicalExposureScore": 74,
  "grade": "EXTREME POLITICAL EXPOSURE",
  "models": {
    "politicalDependency": {
      "score": 85,
      "label": "HEAVILY DEPENDENT",
      "findings": [
        "24 SAM.gov contract records — heavy government contract dependency",
        "$2400M in federal awards — extreme government revenue dependency",
        "14 Federal Register references — high regulatory exposure",
        "7 relevant congressional bills — legislative dependency"
      ]
    },
    "influenceNetwork": {
      "score": 72,
      "label": "DEEP NETWORK",
      "findings": [
        "28 lobbying filings — extensive political influence operation",
        "22 FEC contribution records — major political donor",
        "12 congressional stock trades — high lawmaker interest",
        "4 influence channels active — sophisticated political operation"
      ],
      "channels": ["lobbying", "campaign finance", "congressional trading", "foreign agents"]
    },
    "legislativeThreat": {
      "score": 15,
      "direction": "MILD OPPORTUNITY",
      "findings": [
        "Congressional BULLISH: 9 buys vs 3 sells — lawmakers see opportunity",
        "5 supportive vs 1 restrictive bill(s) — favorable legislative environment",
        "28 lobbying filings — industry actively shaping legislation"
      ]
    },
    "foreignInfluence": {
      "score": 35,
      "riskLevel": "MODERATE FOREIGN RISK",
      "findings": [
        "3 FARA registrations — multiple foreign agent ties",
        "6 foreign corporate entities — complex international structure"
      ]
    },
    "revolvingDoor": {
      "score": 65,
      "label": "REVOLVING DOOR ACTIVE",
      "findings": [
        "High lobbying + government contracts — strong revolving door pattern",
        "FARA registrations + domestic lobbying — international revolving door detected",
        "Congressional stock trading + campaign contributions — deep political-corporate ties",
        "Federal awards + active lobbying — contract-lobbying reinforcement loop",
        "5/5 political influence channels active — revolving door ecosystem"
      ]
    }
  },
  "recommendation": "Extreme political exposure. Multiple foreign influence channels, heavy government dependency, and active revolving door patterns. Critical ESG and compliance review required.",
  "dataSources": {
    "lobbying": 28,
    "fecContributions": 22,
    "congressStockTrades": 12,
    "faraRegistrations": 3,
    "congressBills": 8,
    "federalRegister": 14,
    "samContracts": 24,
    "usaSpending": 18,
    "ofacResults": 0,
    "openSanctions": 0,
    "openCorporates": 12
  }
}

Output fields

| Field | Type | Description |
|-------|------|-------------|
| company | string | Company name as queried |
| politicalExposureScore | number | Composite score 0-100; higher = more exposed |
| grade | string | Label: LOW / MODERATE / HIGH / EXTREME POLITICAL EXPOSURE |
| recommendation | string | Actionable guidance based on composite score tier |
| models.politicalDependency.score | number | Political Dependency Score 0-100 |
| models.politicalDependency.label | string | INDEPENDENT / LIGHTLY / MODERATELY / HEAVILY DEPENDENT |
| models.politicalDependency.findings | string[] | Specific data points contributing to the score |
| models.influenceNetwork.score | number | Influence Network Density Score 0-100 |
| models.influenceNetwork.label | string | MINIMAL / LIGHT / ACTIVE / DEEP NETWORK |
| models.influenceNetwork.channels | string[] | Active influence channels detected |
| models.legislativeThreat.score | number | Legislative Threat/Opportunity Score -100 to +100 |
| models.legislativeThreat.direction | string | SEVERE THREAT / MILD THREAT / NEUTRAL / MILD OPPORTUNITY / STRONG OPPORTUNITY |
| models.foreignInfluence.score | number | Foreign Influence Exposure Score 0-100 |
| models.foreignInfluence.riskLevel | string | MINIMAL / LOW / MODERATE / HIGH FOREIGN RISK |
| models.revolvingDoor.score | number | Revolving Door Index 0-100 |
| models.revolvingDoor.label | string | NO SIGNALS / MILD SIGNALS / REVOLVING DOOR SIGNALS / REVOLVING DOOR ACTIVE |
| dataSources.* | number | Raw record count returned from each data source |

How the scoring models work

Political Dependency Score (0-100)

Measures reliance on federal government as a revenue source. The model assigns points based on SAM.gov contract record count (up to 30 points for 20+ records), USAspending total obligation amount (up to 25 points for awards over $100M), Federal Register mentions as a regulatory dependency proxy (up to 15 points for 10+ entries), and congressional bill relevance (up to 10 points for 5+ bills). The score is capped at 100. Labels: INDEPENDENT (0-9), LIGHTLY DEPENDENT (10-34), MODERATELY DEPENDENT (35-59), HEAVILY DEPENDENT (60-100).

Influence Network Density Score (0-100)

Maps how many political influence channels are simultaneously active. Points are assigned per channel: lobbying (up to 25 points for 20+ filings), FEC contributions (up to 25 points for 20+ records), congressional stock trading (up to 20 points for 10+ trades), FARA registrations (15 points for any presence), with a 10-point multi-channel bonus when 3 or more channels are active. Labels: MINIMAL NETWORK (0-9), LIGHT NETWORK (10-34), ACTIVE NETWORK (35-59), DEEP NETWORK (60-100).

Legislative Threat/Opportunity Score (-100 to +100)

Classifies the current legislative environment using four signal types. Congressional trade buy/sell ratios using a 2:1 majority threshold contribute ±25 points. Bill title keyword analysis (supportive vs restrictive) contributes ±20 points. Federal Register volume contributes -10 points for heavy regulatory presence. Lobbying intensity contributes +10 points when active lobbying indicates industry engagement. Any OFAC or OpenSanctions match immediately contributes -30 points regardless of other signals.

Foreign Influence Exposure Score (0-100)

Detects foreign government and international entanglement. FARA registrations contribute 10-35 points based on count. OFAC sanctions matches contribute a flat 30 points. OpenSanctions matches contribute 20 points. Foreign corporate entities (non-US jurisdictions in OpenCorporates) contribute up to 15 points for 5+ entities. Labels: MINIMAL (0-4), LOW (5-24), MODERATE (25-49), HIGH FOREIGN RISK (50-100).

Revolving Door Index (0-100)

Detects government-corporate personnel flow patterns through behavioral cross-signals rather than direct officer lookups. High lobbying plus government contracts earns 30 points as a "contract-lobbying reinforcement loop" signal. FARA registrations combined with domestic lobbying earn 20 points as an "international revolving door" signal. Congressional stock trading plus FEC contributions earn up to 20 points for "deep political-corporate ties." Federal awards plus active lobbying earn 15 points. Having 4 or more of the 5 influence channels simultaneously active earns an additional 15 points for "revolving door ecosystem" status.

Composite Political Exposure Score

Weighted average: Foreign Influence (25%) + Political Dependency (25%) + Influence Network (20%) + Revolving Door (15%) + Legislative Threat inverted and normalized (15%). The Legislative Threat score is mapped so that maximum opportunity (+100) contributes 0 to the composite and maximum threat (-100) contributes 100, making the composite score consistently directional — higher always means more exposed.

How much does it cost to assess corporate political exposure?

This MCP server uses pay-per-event pricing with no subscription fees. Individual tools cost $2.00 per call. The full influence_network_graph assessment costs $5.00 because it runs all 11 data sources simultaneously.

| Scenario | Tool | Cost |
|----------|------|------|
| Quick test — single scan | political_exposure_scan | $2.00 |
| Single deep assessment | influence_network_graph | $5.00 |
| Portfolio screening (10 companies, quick scan) | political_exposure_scan x10 | $20.00 |
| Portfolio assessment (10 companies, full) | influence_network_graph x10 | $50.00 |
| Quarterly monitoring (50 companies, full) | influence_network_graph x50 | $250.00 |

You can set a maximum spending limit per run in Apify to cap costs. The server checks this limit before every tool execution and returns a graceful error if the limit is reached. Apify's free tier includes $5 of monthly platform credits, covering one political_exposure_scan or partial credit toward a full assessment at no cost.

Using the API directly

Python

```python
from apify_client import ApifyClient

client = ApifyClient("YOUR_API_TOKEN")

run = client.actor("ryanclinton/corporate-political-exposure-mcp").call(run_input={})