Agenten stärken
Der eigene Befehl des Herausgebers. Kein PlugMCP-Endpunkt.
Host a copy with our keys and logs.
uvx oracle3==1.2.2Claude Code hat keinen Installationslink für einen lokalen Befehl. Kopieren Sie den Befehl unten und führen Sie ihn in einem Terminal aus.
ORACLE3_MCP_LEDGER=<value> claude mcp add io.github.yichengyang-ethan-oracle3 -- uvx oracle3==1.2.2In einem Terminal ausführen.
{ "mcpServers": { "io.github.yichengyang-ethan-oracle3": { "command": "uvx", "args": [ "oracle3==1.2.2" ], "env": { "ORACLE3_MCP_LEDGER": "<value>" } } }}claude_desktop_config.json
Remote-URL oder ein Befehl auf Ihrem Rechner? Worin sich die beiden Installationen unterscheiden
Auf PlugMCP ausführen
A new server you create. It is not this listing's official endpoint. Use the publisher install when you want their URL and their credentials.
StartenVom Herausgeber
Build prediction-market agents that discover markets, analyze opportunities, and test strategies.
Connect AI agents to Kalshi and Polymarket through MCP. Give them reusable Skills for market research and strategy development, then build on a Python engine for paper and live execution.
13 MCP tools · 6 agent Skills · Kalshi & Polymarket data · Python 3.10+
Kalshi and Polymarket data, venue fees, no-arbitrage checks and paper execution for AI agents.
Public Kalshi and Polymarket market reads need no exchange credentials. MCP provides market data, calculations, and a local paper ledger; real-order execution uses the separate CLI/Python engine.
Install the current development checkout:
git clone https://github.com/YichengYang-Ethan/oracle3-prediction-market-agent.git
cd oracle3-prediction-market-agent
python -m venv .venv
.venv/bin/python -m pip install -e .
Connect it to Claude Code from this directory:
claude mcp add oracle3 -- "$PWD/.venv/bin/oracle3-mcp"
For other MCP clients, configure the absolute path to .venv/bin/oracle3-mcp as the command with an empty argument list. On Windows, use .venv\Scripts\python.exe and .venv\Scripts\oracle3-mcp.exe.
Find Kalshi and Polymarket contracts about the next Fed rate decision. Compare their outcome definitions, resolution rules, and order-book quotes. Summarize the markets with their IDs, prices, and the assumptions needed to compare them.
For a calculation that works offline after installation:
Use Oracle3 to check an implication: A implies B. A has a YES bid of $0.60 and B has a YES ask of $0.55. Use the default Kalshi taker fee schedule and 100 contracts per leg. Show the basket, gross edge, fees, and net edge.
The synthetic example returns $5.00 gross edge − $3.4125 fees = $1.5875 calculated net edge, conditional on the supplied implication and quotes. See exact inputs and output.
Relationship checks cover implication, exclusivity, complement, same event, and event sum. They expose assumptions with their results, giving agents and researchers a common calculation layer.
The repository includes six workflows that help agents move from individual tool calls to complete tasks:
Claude Code project copies are included in .claude/skills/. Open the checkout as your project, or use your client's supported Skill installation mechanism. Skill setup.
For AI agents: start with llms.txt for task routing, the MCP reference for tool schemas, and AGENTS.md for contributing code.
Use the same project for research scripts, agent strategies, and engine operations:
.venv/bin/oracle3 market search --exchange kalshi --query "fed" --json
.venv/bin/oracle3 market search --exchange polymarket --query "fed decision" --json
Oracle3 combines a concrete decision environment with structured tools, observable state, and a software validation harness. This supports work on agent interfaces, workflow guidance, and reproducible evaluation.
CI runs automated tests and checks the built package through real MCP sessions on SDK 1.x and 2.x. The harness documentation explains the reproducible checks. Model-level agent evaluation is a next research direction.
Help build better agent workflows, labeled evaluation tasks, strategy examples, and client integrations. New venue, data-provider, and wallet integrations start in oracle3-extras.
Oracle3 began as ulab-uiuc/oracle3, developed by Yicheng Yang and Haofei Yu at U Lab (University of Illinois Urbana-Champaign). It also bundles the lab's Coinjure package. Yicheng Yang continues to develop and maintain the extensions in this repository. Read the project story and contributions.
We acknowledge the foundational work of the Coinjure team at the University of Illinois Urbana-Champaign—Haofei Yu, Yicheng Yang, Yuxiang Liu, and Prof. Jiaxuan You—on which Oracle3 builds.
For software citation, use CITATION.cff and Zenodo. For the pricing model, see the working paper.
This software is for research and education. Trading involves financial risk.
Kalshi and Polymarket data, venue fees, no-arbitrage checks and paper execution for AI agents.
{ "mcpServers": { "io.github.yichengyang-ethan-oracle3": { "command": "uvx", "args": [ "oracle3==1.2.2" ], "env": { "ORACLE3_MCP_LEDGER": "<value>" } } }}Tools
Noch keine Tool-Liste. Remote-Server erhalten eine nach dem ersten Health-Check.
FAQ
Kopieren Sie auf dieser Seite die stdio-Konfiguration für Cursor, Claude Desktop, Claude Code oder VS Code. Sie nutzt die Installation des Herausgebers, keinen PlugMCP-Proxy.
Der Herausgeber nennt diese Umgebungsvariablen: ORACLE3_MCP_LEDGER.
Oracle3 Prediction Market Arbitrage Checks läuft lokal über stdio.
Zuletzt geprüft: 2026-11-13. Kalshi and Polymarket data, venue fees, no-arbitrage checks and paper execution for AI agents.