Political Influence Network MCP

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Quick Start

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

{
  "mcpServers": {
    "political-influence-network-mcp": {
      "url": "https://ryanclinton--political-influence-network-mcp.apify.actor/mcp"
    }
  }
}

---

Political influence network analysis for AI assistants — map donor-politician-legislation relationships, detect conflicts of interest, and trace dark money flows through 8 US government data sources in a single MCP tool call. Built for compliance teams, investigative researchers, and AI agents that need structured political intelligence without raw API complexity.

This MCP server orchestrates 8 public-data actors in parallel: FEC campaign finance filings, congressional stock trading disclosures, Congress.gov bill tracking, Federal Register regulatory actions, nonprofit organization filings, USAspending federal contracts, OpenCorporates corporate registries, and Wikipedia background context. Each tool call assembles a bipartite political graph, runs graph algorithms across it, and returns structured JSON ready for downstream analysis or AI reasoning.

⬇️ What data can you extract?

| Data Point | Source | Example |
|---|---|---|
| 💰 Campaign contributions | FEC Finance | Donor → PAC → candidate, $50,000, cycle 2024 |
| 📈 Congressional stock trades | Congress Stock Tracker | Rep. Jane Doe, MSFT, $15K–50K, Jan 2024 |
| 📋 Legislation & sponsorships | Congress Bill Search | HR 4201, Defense Procurement Act, introduced 2024-03 |
| 📜 Federal regulatory actions | Federal Register Search | EPA Final Rule, petroleum industry, 2024-02-14 |
| 🏛 Nonprofit filings & finances | Nonprofit Explorer | Horizon Policy Fund 501(c)(4), revenue $4.2M |
| 🏗 Federal contract awards | USAspending Search | Apex Defense LLC, DoD, $8.7M contract 2024-01 |
| 🏢 Corporate registrations | OpenCorporates Search | Meridian Holdings Inc., Delaware, incorporated 2019 |
| 🔗 Background entity context | Wikipedia | Lobbying firm histories, PAC profiles |
| 📊 PageRank influence scores | Graph computation | Entity "Apex Defense", pageRank 0.0412, cluster 2 |
| ⚠️ Conflict of interest flags | Cross-source correlation | Stock trade after legislation, severity 0.87 |
| 🕵️ Dark money flow paths | Multi-hop tracing | Corp → 501(c)(4) → PAC → candidate, opacity 0.90 |
| 🎯 Political risk assessments | Multi-factor scoring | Entity score 0.68, risk level "high" |

Why use Political Influence Network MCP?

Manually tracing political influence across FEC filings, congressional disclosures, federal spending databases, and nonprofit registries takes weeks of research. Cross-referencing eight separate APIs, normalizing entity names, computing temporal correlations, and identifying network clusters is beyond what most analysts can do by hand.

This MCP automates the entire process. Connect it to Claude, Cursor, or any MCP-compatible client and ask natural-language questions — the server handles data retrieval, graph construction, and algorithmic analysis.

- Scheduling — run political risk monitoring on a weekly cadence to track evolving influence networks
- API access — trigger analysis from Python, JavaScript, or any HTTP client via the Apify API
- Proxy rotation — all upstream actor calls use Apify's infrastructure without IP management
- Monitoring — receive Slack or email alerts when influence report risk levels change
- Integrations — connect findings to Zapier, Make, Google Sheets, or compliance workflow tools

Features

- PageRank influence scoring — 20-iteration damped PageRank (damping factor 0.85) on the full political graph, ranking entities by structural importance across the donor-politician-legislation network
- Bipartite graph construction — builds a directed multi-type graph with 8 node types (politician, committee, donor, corporation, nonprofit, agency, bill, contract) and 7 edge types (donates, sponsors, trades, awards, lobbies, regulates, employs)
- Temporal Granger causality — trace_money_to_legislation computes a causal score combining a temporal proximity component (donation-to-bill gap in days, up to 365-day window) and an amount magnitude component, weighted 60/40, to surface suspicious timing patterns
- Conflict of interest detection — cross-correlates stock trade edges against sponsored legislation edges for each politician node; separately screens donor-contribution → contract-award correlations using name-hash matching
- Dark money path tracing — multi-hop graph traversal through nonprofit intermediaries; assigns opacity scores by IRS subsection (501(c)(4) = 0.90, 501(c)(6) = 0.70, other = 0.40); covers both nonprofit chains and corporation-to-politician two-hop paths
- Revolving door analysis — identifies politician nodes with simultaneous agency and corporate edge connections; scores influence potential from connection count and a seeded hash function for deterministic reproducibility across runs
- Lobbying ROI calculation — computes return on lobbying investment as (legislative wins × $100K base value + contract value) ÷ total lobbying spend per entity; ranks all corporations and nonprofits by ROI estimate
- Multi-factor risk scoring — 6-factor weighted political risk model: donation concentration (0.15), conflict exposure (0.20), dark money proximity (0.20), contract dependency (0.15), network centrality (0.15), revolving door exposure (0.15)
- 5-minute TTL cache — all upstream actor calls are cached for 5 minutes per query, so repeated tool calls within a session do not incur redundant API costs
- Up to 500 items per source — each upstream actor is called with a 500-item limit and a 180-second timeout; failed sources return empty arrays without crashing the tool
- Both SSE and Streamable HTTP transports — supports legacy /sse + /messages SSE protocol and modern /mcp Streamable HTTP for compatibility across MCP clients
- Selective source querying — each tool defaults to the most relevant subset of sources; advanced users can specify any combination of the 7 source groups to control cost and depth

Use cases for political influence network analysis

Corporate political risk assessment

Compliance officers at financial institutions, law firms, and ESG-focused investment funds need to understand political exposure before onboarding clients or making investments. Asking compute_political_risk_score for a corporation surfaces its campaign finance contributions, dark money proximity, revolving door relationships, and contract dependency in a single structured response — replacing manual FARA, FEC, and USAspending searches.

Investigative journalism and research

Journalists investigating regulatory capture, pay-to-play contracting, or congressional trading patterns can ask the MCP to trace_money_to_legislation for a specific industry or politician. The Granger causality scores highlight the donation-to-bill pairings with the tightest temporal proximity and largest dollar amounts, providing data-backed leads for further investigation.

Lobbying effectiveness benchmarking

Policy affairs teams at trade associations and corporations use score_lobbying_influence to understand how their lobbying ROI compares to peers. The tool ranks all entities in the network by estimated return, showing legislative wins per dollar spent alongside contract awards received.

Congressional trading oversight

Ethics watchdog organizations and institutional investors tracking STOCK Act compliance can use detect_conflict_of_interest to screen a legislator for overlaps between their disclosed stock trades, their major donors, and the bills they sponsor — with severity scores ranked by correlation strength.

AI agent political intelligence workflows

AI agents built for due diligence, regulatory monitoring, or geopolitical risk analysis can call generate_influence_report as a single tool to get a complete structured briefing — findings by category, overall risk level, and specific recommendations — without chaining multiple tool calls.

Nonprofit and dark money investigations

Anti-corruption researchers and journalists investigating dark money flows can use detect_dark_money_flows to trace multi-hop funding paths through 501(c)(4) and 501(c)(6) organizations. The opacity score distinguishes social welfare organizations (high opacity) from more transparent vehicles.

How to connect Political Influence Network MCP

Step 1: Connect your MCP client

Add the server URL to your client's MCP configuration. No API key is needed in the URL itself — the server runs on Apify's infrastructure.

Step 2: Choose your tool

Eight tools cover distinct analytical tasks. Start with generate_influence_report for a complete overview, or use targeted tools like detect_conflict_of_interest for specific screening.

Step 3: Enter a query

Type the name of a politician, corporation, PAC, or policy topic. The server queries the relevant data sources in parallel.

Step 4: Review structured results

Each tool returns structured JSON with scored entities, ranked findings, and specific recommendations suitable for AI reasoning or direct export.

MCP tools

| Tool | Default sources | Price | Description |
|------|----------------|-------|-------------|
| map_influence_network | finance, trading, legislation, spending | $0.045 | PageRank-based network map of donors, politicians, corporations |
| detect_conflict_of_interest | finance, trading, legislation, spending | $0.045 | Screen politicians for stock trade / donation / legislation conflicts |
| trace_money_to_legislation | finance, legislation | $0.050 | Granger causality analysis: donations → legislative actions |
| analyze_revolving_door | finance, regulation, corporate | $0.045 | Government-to-private sector transition detection with scoring |
| score_lobbying_influence | finance, legislation, spending, nonprofits | $0.045 | Lobbying ROI: spend vs legislative wins and contract awards |
| detect_dark_money_flows | finance, nonprofits, corporate | $0.050 | Multi-hop opaque funding path tracing through nonprofits |
| compute_political_risk_score | all 7 sources | $0.045 | Multi-factor weighted political risk score (6 factors) |
| generate_influence_report | all 7 sources | $0.050 | Full political influence intelligence report with recommendations |

Tool input parameters

Each tool accepts the same two parameters:

| Parameter | Type | Required | Default | Description |
|-----------|------|----------|---------|-------------|
| query | string | Yes | — | Politician, corporation, PAC, or policy topic to investigate |
| sources | array of strings | No | Varies by tool | Data source groups: "finance", "trading", "legislation", "regulation", "nonprofits", "spending", "corporate" |

Tool input examples

Screen a politician for conflicts of interest:

{

  "query": "Senator Patricia Hartwell",

  "sources": ["finance", "trading", "legislation"]

}

Trace money flows for an industry topic:

{

  "query": "pharmaceutical pricing regulation",

  "sources": ["finance", "legislation", "nonprofits"]

}

Full political risk report for a corporation:

{

  "query": "Meridian Defense Systems",

  "sources": ["finance", "trading", "legislation", "regulation", "nonprofits", "spending", "corporate"]

}

Dark money investigation — minimal cost:

{

  "query": "Horizon Policy Fund",

  "sources": ["finance", "nonprofits", "corporate"]

}

Input tips

- Use full proper names — "Senator John Cornyn" returns better results than "Cornyn" because FEC and Congress.gov match on full names
- Use policy topics for industry-level analysis — queries like "defense procurement" or "pharmaceutical pricing" retrieve broader network data than single-entity queries
- Restrict sources to reduce cost — trace_money_to_legislation only needs ["finance", "legislation"]; adding all 7 sources triples the data but rarely changes the top findings
- Call generate_influence_report first — it queries all sources and the 5-minute TTL cache means subsequent targeted tool calls on the same query are essentially free within the session
- Interpret scores as signals, not verdicts — causal scores above 0.6 and risk levels of "high" or "critical" warrant deeper investigation, not automatic conclusions

Output example

generate_influence_report for "Apex Defense Corp":

{
  "findings": [
    {
      "category": "Campaign Finance",
      "finding": "14 large donations detected (>$10K each)",
      "severity": "high",
      "evidence": "Total large donation volume: $2847000",
      "recommendation": "Review FEC filings for bundling patterns and coordination indicators"
    },
    {
      "category": "Dark Money",
      "finding": "4 dark money vehicle(s) identified (501(c)(4)/(6))",
      "severity": "critical",
      "evidence": "Entities: Horizon Policy, National Growth, Freedom Coa, AmeriFirst Fu",
      "recommendation": "Trace funding flows through these entities for donor disclosure gaps"
    },
    {
      "category": "Congressional Trading",
      "finding": "7 legislator(s) with active stock trading",
      "severity": "high",
      "evidence": "23 trade(s) detected across portfolio",
      "recommendation": "Cross-reference trade timing with committee actions and legislation votes"
    },
    {
      "category": "Federal Spending",
      "finding": "Top contractor: Apex Defense Corp ($8750000)",
      "severity": "high",
      "evidence": "11 total contract award(s) in network",
      "recommendation": "Audit contractor-donor overlap and no-bid award patterns"
    },
    {
      "category": "Revolving Door",
      "finding": "6 politician(s) with agency connections",
      "severity": "high",
      "evidence": "Individuals: Sen. Hartwell, Rep. Morrison, Dep. Sec. Wynn",
      "recommendation": "Review cooling-off period compliance and post-government employment restrictions"
    }
  ],
  "overallRisk": "critical",
  "totalInfluenceValue": 11597000,
  "recommendations": [
    "Initiate comprehensive campaign finance audit",
    "Review STOCK Act compliance for flagged legislators",
    "Investigate 501(c)(4) donor disclosure gaps",
    "Audit federal procurement for pay-to-play patterns"
  ]
}

Output fields

generate_influence_report

| Field | Type | Description |
|-------|------|-------------|
| findings[] | array | Ordered list of intelligence findings by severity |
| findings[].category | string | Category: Campaign Finance, Congressional Trading, Dark Money, Federal Spending, Revolving Door |
| findings[].finding | string | Human-readable finding summary |
| findings[].severity | string | "low", "medium", "high", or "critical" |
| findings[].evidence | string | Supporting data points |
| findings[].recommendation | string | Specific action recommendation |
| overallRisk | string | Aggregate risk level: "low" / "medium" / "high" / "critical" |
| totalInfluenceValue | number | Sum of all donation and contract amounts in USD |
| recommendations[] | array | Prioritized list of investigation actions |

map_influence_network

| Field | Type | Description |
|-------|------|-------------|
| nodes[] | array | Top 30 entities by PageRank, sorted descending |
| nodes[].entity | string | Entity name |
| nodes[].role | string | Node type: politician, donor, corporation, nonprofit, agency, bill, contract |
| nodes[].pageRank | number | PageRank score (6 decimal places) |
| nodes[].inDegree | number | Count of incoming edges |
| nodes[].outDegree | number | Count of outgoing edges |
| nodes[].cluster | number | Connected component cluster index |
| totalEntities | number | Total nodes in full network |
| totalConnections | number | Total edges in full network |
| networkDensity | number | Graph density: 2E / (N × (N-1)) |
| topInfluencers | array | Top 10 entity names by PageRank |

detect_conflict_of_interest

| Field | Type | Description |
|-------|------|-------------|
| conflicts[] | array | Detected conflicts, sorted by severity descending |
| conflicts[].politician | string | Politician name |
| conflicts[].entity | string | Counterparty entity name |
| conflicts[].conflictType | string | stock_trade_after_legislation, donor_contract_award, committee_oversight_conflict, family_financial_interest |
| conflicts[].severity | number | Severity score 0–1 |
| conflicts[].evidence | string | Supporting evidence string |
| totalScreened | number | Total politicians screened |
| conflictCount | number | Total conflicts detected |
| avgSeverity | number | Mean severity score |

trace_money_to_legislation

| Field | Type | Description |
|-------|------|-------------|
| links[] | array | Donation-legislation links with causal scores above 0.3 |
| links[].donor | string | Donor entity name |
| links[].recipient | string | Recipient politician name |
| links[].amount | number | Donation amount in USD |
| links[].bill | string | Associated bill name or ID |
| links[].temporalGap | number | Days between donation and bill introduction |
| links[].causalScore | number | Granger causality proxy score 0–1 (0.6× temporal + 0.4× amount) |
| totalTracked | number | Total donation edges analyzed |
| suspiciousCount | number | Links with causalScore > 0.6 |
| totalFlowVolume | number | Sum of all donation amounts in suspicious links |

compute_political_risk_score

| Field | Type | Description |
|-------|------|-------------|
| assessments[] | array | Top 30 entities by risk score |
| assessments[].entity | string | Entity name |
| assessments[].riskScore | number | Weighted risk score 0–1 |
| assessments[].riskLevel | string | "low" (<0.25), "medium" (<0.50), "high" (<0.75), "critical" (≥0.75) |
| assessments[].factors[] | array | 6 factor breakdown with individual scores |
| assessments[].factors[].factor | string | Factor name: donation_concentration, conflict_exposure, dark_money_proximity, contract_dependency, network_centrality, revolving_door |
| assessments[].factors[].weight | number | Factor weight in composite score |
| assessments[].factors[].score | number | Factor score 0–1 |
| avgRisk | number | Mean risk score across all assessed entities |
| criticalCount | number | Count of entities with riskLevel "critical" |

How much does it cost to analyze political influence networks?

Political Influence Network MCP uses pay-per-event pricing — you pay per tool call. Platform compute costs are included.

| Tool | Price per call |
|------|---------------|
| map_influence_network | $0.045 |
| detect_conflict_of_interest | $0.045 |
| trace_money_to_legislation | $0.050 |
| analyze_revolving_door | $0.045 |
| score_lobbying_influence | $0.045 |
| detect_dark_money_flows | $0.050 |
| compute_political_risk_score | $0.045 |
| generate_influence_report | $0.050 |

Typical session cost scenarios:

| Scenario | Tool calls | Estimated cost |
|----------|-----------|----------------|
| Quick check (one tool) | 1 | $0.045–$0.050 |
| Targeted analysis (3 tools) | 3 | $0.135–$0.150 |
| Full investigation (all 8 tools) | 8 | $0.370 |
| Daily monitoring (1 report/day × 30 days) | 30 | $1.50 |
| Weekly team workflow (5 queries/week × 4 weeks) | 20 | $0.90–$1.00 |

The 5-minute TTL cache means calling multiple tools on the same query within a session reuses upstream data — only the first tool call for a query incurs upstream actor costs. You can set a maximum spending limit per run in the Apify console to cap costs automatically.

Compare this to commercial political intelligence platforms charging $500–$2,000/month for similar data. With this MCP, most teams spend under $5/month for routine monitoring workflows.

How to connect this MCP server

Claude Desktop

Add to your claude_desktop_config.json:

{
  "mcpServers": {
    "political-influence-network": {
      "url": "https://political-influence-network-mcp.apify.actor/mcp"
    }
  }
}

Cursor

Add to your Cursor MCP settings (~/.cursor/mcp.json):

{
  "mcpServers": {
    "political-influence-network": {
      "url": "https://political-influence-network-mcp.apify.actor/mcp"
    }
  }
}

Windsurf / Cline / other MCP clients

Use the Streamable HTTP endpoint:

https://political-influence-network-mcp.apify.actor/mcp

Or the legacy SSE endpoint for clients that require it:

https://political-influence-network-mcp.apify.actor/sse

Python (via Apify API)

```python
from apify_client import ApifyClient

client = ApifyClient("YOUR_API_TOKEN")