GovGreed

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).

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.

Tool	What it does
account_info	Show your current GovGreed account: tier, daily quota, remaining calls.
top_signals	Top-ranked congressional trading signals (7-layer convergence scoring); deduplicated per politician+ticker.
herd_signals	Moments when 3+ politicians converge on the same ticker, deduplicated per (ticker, window).
whale_opportunities	EV-ranked bills × tickers × insider activity, cross-checked against political influence to drop junk tickers.
top_predictions	Forward-looking predictions from GovGreed's four prediction engines, grouped per (politician, ticker).
search_politicians	Fuzzy politician search; resolves "Pelosi" → bioguide_id for follow-up calls.
politician_profile	Full politician profile with all four scores (greediness / influence / conflict / predicted-corruption).
politician_conflict_score	Conflict-of-interest score 0-100 — the most objective, vote-level evidence score.
bill_intelligence	Full bill baseball card: impacts, predictions, markup history, exec trades, carveouts.
bill_carveouts	Line-item carveouts: each $ amount tied to a recipient, section-cited, with ticker mapping.
search_bills	Fuzzy bill search by title or nickname (NDAA, CHIPS, IRA, etc.).
company_profile	Full company iron-triangle profile: congressional owners, lobbyists, affected-by bills, insider activity.
company_political_influence	0-100 political influence score for a ticker, broken down by lobbying / contributions / pharma / LDA spend.
sector_positioning	Congressional buy/sell flow per sector over N days.
search_donors	Fuzzy donor search with fingerprint-aggregation across FEC name variants.
search_donors_raw	Raw FEC donor search — one row per name-string-as-filed, no merging.
donor_profile	Full donor profile, fingerprint-aggregated, with deduplicated recent gifts.
oge_cabinet_trades	Trump-administration cabinet/senior-WH-staff financial disclosures and transactions (OGE 278e + 278-T).

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?"

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Not financial advice. Data from public federal disclosures.