Capitol Trades MCP

A Model Context Protocol (MCP) server that surfaces US congressional stock-trade disclosures from capitoltrades.com — no API key required.

It exposes 23 tools for querying, filtering, ranking, and exporting politician trades, built on FastMCP.

⚠️ Not investment advice. This server reports publicly disclosed transactions for research and transparency only.

How it works

capitoltrades.com is a Next.js (App Router) site on Vercel. Instead of scraping presentational HTML — which breaks on every redesign — this server reads the structured trade records embedded in the page's React Server Component flight stream (self.__next_f.push(...) script chunks), where each trade is a clean JSON object. That keeps the parser resilient to cosmetic changes and exposes richer fields than the rendered table: per-share price, reportingGap, estimated value, owner, sector, comment, and stable ids.

Related MCP server: Financial Analysis MCP Server

Tools

Core

• get_politician_trades — filtered trade list (issuer, politician, party, type, window)

• get_top_traded_assets — most-traded assets, ranked by trade count

• get_politician_stats — per-politician breakdown

• get_asset_stats — per-asset breakdown

• get_buy_momentum_assets — assets where politicians are net buyers

• get_party_buy_momentum — net buyers split into consensus / Democrat / Republican

Feeds & lookups

• get_recent_trades — latest disclosures across all politicians

• search_politicians — resolve a name to its Capitol Trades id (+ party/state/stats)

• search_issuers — resolve a ticker/name to issuer id, sector and stats

• get_trade_detail — full record for a single transaction id

Filter dimensions

• get_trades_by_sector · get_trades_by_state · get_trades_by_owner · get_trades_by_chamber

Rankings & analytics

• get_largest_trades — biggest trades by estimated value

• get_most_active_politicians — ranked by trade count then value

• get_sector_momentum — net buy/sell flow by sector

• get_politician_net_positions — per-asset net buying/selling for one member

Compliance / disclosure quality

• get_late_filings — trades disclosed slower than the STOCK Act's 45-day window

• get_disclosure_gap_leaderboard — members ranked by average reporting gap

Cross-referencing

• get_congress_activity_for_tickers — batch summary for a watchlist of tickers

Output / convenience

• export_trades_csv — filtered trades as CSV text

• get_trades_in_date_range — trades within an arbitrary YYYY-MM-DD range

Requirements

• Python 3.10+

• fastmcp, httpx, beautifulsoup4, lxml

Installation

git clone https://github.com/jsconiers/capitol-trades-mcp.git
cd capitol-trades-mcp
python3 -m venv .venv
source .venv/bin/activate
pip install -r requirements.txt

🍏 Apple Silicon note: install with the virtual-env's own pip (as above), not a Rosetta/x86_64 uv — otherwise native wheels such as pydantic-core can fail to load with an "incompatible architecture" error.

It also runs dependency-free via uv thanks to inline PEP 723 metadata:

uv run capital_trades_mcp.py

Claude Desktop configuration

Add to claude_desktop_config.json (see claude_desktop_config.example.json), using absolute paths:

{
  "mcpServers": {
    "capitol-trades": {
      "command": "/absolute/path/to/capitol-trades-mcp/.venv/bin/python",
      "args": ["/absolute/path/to/capitol-trades-mcp/capital_trades_mcp.py"],
      "env": {}
    }
  }
}

Restart Claude Desktop. No API key or credentials are required.

Usage examples

• "What are the most recent congressional trades?" → get_recent_trades

• "Show me Nancy Pelosi's trades over the last year." → get_politician_stats

• "Who in Congress has been buying NVDA?" → get_asset_stats

• "Which members filed late this quarter?" → get_late_filings

• "Cross-reference my watchlist [AAPL, MSFT, NVDA] against Congress." → get_congress_activity_for_tickers

Testing

python test_port.py

Offline tests cover the flight-JSON parser, the issuer/politician parsers, every aggregation/filter helper, CSV export, and tool registration. No network needed.

Notes & limitations

• Filtering windows: filter/ranking tools pull up to ~500 of the most recent trades in the look-back window and filter client-side, so very rare sectors/states may be under-represented over long windows.

• Window cache: repeated analytics on the same look-back window share one fetch for ~60 seconds.

• N/A tickers: some disclosed issuers (private funds, certain instruments) carry no ticker in the source data.

• Be courteous: the server delays slightly between paginated requests. Respect capitoltrades.com's terms of service.

Credits

• Data: Capitol Trades (2iQ Research).

• Python/FastMCP port inspired by the Node/TypeScript project anguslin/mcp-capitol-trades. Review the upstream project's license before redistributing.

License

MIT for this Python port — see LICENSE. Capitol Trades data and the upstream project remain under their respective terms.