GovGreed MCP

GovGreed MCP is a Model Context Protocol server that gives Claude (and any MCP-aware client) native, one-line tool access to GovGreed's congressional-trading intelligence API. Ask Claude about a politician's conflict-of-interest score, today's A+ trading signals, which bills carve out money for a specific ticker, or how a company spends its political-influence dollars — and Claude answers from live GovGreed data instead of guessing. It wraps 18 tools spanning signals, predictions, politicians, bills, companies, donors, and executive-branch (OGE) disclosures.

Get an API key

Get a free API key at govgreed.com.

• Free tier: 100 calls/day for the first week, then 20/day.

• Founders: $24.50/mo = 750 calls/day, price locked forever.

Set it as the GOVGREED_API_KEY environment variable (shown in the install snippets below).

Related MCP server: Polymarket MCP Server

Install

uvx (recommended)

Add this to your MCP client config (e.g. claude_desktop_config.json) inside "mcpServers":

"govgreed": {
  "command": "uvx",
  "args": ["--from", "git+https://github.com/mmamodelai/govgreed-mcp", "govgreed-mcp"],
  "env": { "GOVGREED_API_KEY": "your_key" }
}

Claude Code (one-liner)

claude mcp add govgreed --env GOVGREED_API_KEY=your_key -- uvx --from git+https://github.com/mmamodelai/govgreed-mcp govgreed-mcp

pipx (alternative)

pipx install git+https://github.com/mmamodelai/govgreed-mcp

Then point your MCP client at the installed entry point:

"govgreed": {
  "command": "govgreed-mcp",
  "env": { "GOVGREED_API_KEY": "your_key" }
}

Tools

All 18 tools return a standard envelope with an as_of timestamp and a data payload.

Score-name semantics

GovGreed surfaces four different scores on a politician. They measure different things and should not be compared 1:1 — pick the right one:

• greediness — Behavioral: how aggressively they trade (volume × frequency × recency). High = active trader, not necessarily corrupt. (Pelosi ≈ 44.)

• influence_score — Structural: how much money/lobbying flows to them (donor flows, lobbying alignment, PAC industry tags). High = well-funded. (Pelosi ≈ 90.)

• conflict_score — Vote-level: bills they voted YES on while holding affected stock, weighted by impact and timing. Subscores: trade_activity, late_filing, committee_alignment, donor_alignment, voted_holdings. (Pelosi ≈ 22.)

• predicted_corruption_risk — Forward-looking ML score from the LLM behavioral analysis. (Pelosi ≈ 85.)

"How concerning is X right now" → conflict_score (most objective). "How active is their trading" → greediness. "Structural pull in DC" → influence_score.

Donor fingerprinting

The FEC stores donor names as filed, so one person fractures into many records — e.g. Robert L. Mercer appears as MERCER, ROBERT L., MERCER, ROBERT MR., MERCER, ROBERT L, MERCER, ROBERT L. MR., MERCER, ROBERT L MR (~$4.66M combined). search_donors / donor_profile return fingerprinted records that merge variants by canonical name + state + employer, with variants_merged, match_method, and match_confidence on every result. Use search_donors_raw to see the unmerged FEC name strings. Merging is deliberately not done across different employer, different state, or different generational suffix (JR vs SR is preserved).

Example prompts

Paste any of these into Claude once the server is connected:

• "What are today's A+ congressional signals?"

• "What's Pelosi's conflict score and why?"

• "Which bills have carveouts for $NVDA?"

Not financial advice. Data from public federal disclosures.