# Kalshi Multi-Agent Research MCP Server

A Model Context Protocol (MCP) server that exposes Kalshi prediction market
data as tools for Claude Cowork. Designed to power a multi-agent research
pipeline where specialist sub-agents (trend, orderbook, news, base-rate,
fundamentals) independently analyze markets and produce confidence-rated
trade signals.

## Architecture

- Python MCP server built on FastMCP (streamable HTTP transport)
- Read-only Kalshi API integration (RSA-PSS signed requests)
- Deployed as a remote Claude connector via ngrok tunnel
- Orchestrated by a Cowork prompt that spawns parallel specialist agents

## Tools exposed

- `list_markets` — filter open/closed/settled markets
- `get_market` — detail on a single market
- `get_orderbook` — bid/ask depth
- `get_recent_trades` — recent fill history
- `get_candlesticks` — historical OHLC data
- `web_research` — scrape external context

## Setup

1. Install dependencies: `uv sync`
2. Create `.env` with `KALSHI_KEY_ID` and `KALSHI_PRIVATE_KEY_PATH`
3. Place your Kalshi private key at the path specified in `.env`
4. Run: `uv run python server.py`
5. Expose via ngrok, add as a custom connector in Claude Desktop

## Disclaimer

Research tool only. Not financial advice. Prediction markets carry real
financial risk; AI-generated confidence scores are one signal, not a
recommendation to trade.