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Kalshi Multi-Agent Research MCP Server

by senshile
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Python
Remote

# 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

Related MCP server: MCP Prediction Markets

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.

This server cannot be deployed

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Maintenance

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