How do I install Oracle3 Prediction Market Arbitrage Checks?

Copy the stdio config for Cursor, Claude Desktop, Claude Code, or VS Code on this page. It uses the publisher's install, not a PlugMCP proxy.

Does Oracle3 Prediction Market Arbitrage Checks need an API key?

The publisher lists these environment variables: ORACLE3_MCP_LEDGER.

What transport does Oracle3 Prediction Market Arbitrage Checks use?

Oracle3 Prediction Market Arbitrage Checks runs locally over stdio.

When was Oracle3 Prediction Market Arbitrage Checks last checked?

Last checked 2026-11-09. Kalshi and Polymarket data, venue fees, no-arbitrage checks and paper execution for AI agents.

Oracle3 Prediction Market Arbitrage Checks MCP server

Power your agent

Add this server

The publisher's own command. Not a PlugMCP endpoint.

Add to Cursor Add to VS Code Run on PlugMCP

Host a copy with our keys and logs.

Local command

uvx oracle3==1.2.2

Credentials from the publisher

ORACLE3_MCP_LEDGER — Path of the local paper-trading ledger (default ~/.oracle3/mcp_paper_ledger.json)

Claude Code has no install link for a local command. Copy the command below and run it in a terminal.

shClaude Code

1ORACLE3_MCP_LEDGER=<value> claude mcp add io.github.yichengyang-ethan-oracle3 -- uvx oracle3==1.2.2

Run in a terminal.

jsonClaude Desktop

1{2  "mcpServers": {3    "io.github.yichengyang-ethan-oracle3": {4      "command": "uvx",5      "args": [6        "oracle3==1.2.2"7      ],8      "env": {9        "ORACLE3_MCP_LEDGER": "<value>"10      }11    }12  }13}

claude_desktop_config.json

Local · pypiPublisher installStep-by-step: add this in Claude

Remote URL or a command on your machine? How the two installs differ

Run on PlugMCP

Host a copy with our keys and logs.

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.

Launch

Server facts

Local

Transport: stdio
Version: 1.2.2
License

Tools

No tool list yet. Remote servers get one after the first health check.

FAQ

Questions

Oracle3

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+

Quickstart · MCP tools · Agent Skills · Project and contributions · Documentation

Why Oracle3

A connected research workflow. Discover contracts, inspect their rules and order books, compare related outcomes, and track simulated positions.
Calculations that include trading costs. Five relationship checks return candidate baskets, per-leg fees, and calculated net edge.
Tools and workflows for agents. Use MCP for structured access and Skills for repeatable research, strategy authoring, and operations.
An engine you can extend. Build strategies with Python interfaces, risk controls, and paper/live venue adapters.
A foundation for experiments. Reproducible software checks, automated tests, and installed-package validation make the implementation inspectable.

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.

Quickstart

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.

Client configuration and first tool call · Published-package installation

Give your agent a first task

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.

What agents can do

Task	Tools	Result
Discover markets	`search_markets`, `get_market`	Contracts, metadata, and available resolution rules
Inspect market conditions	`get_quote`, `get_orderbook`	Prices, fee information, and displayed depth
Analyze related contracts	`check_constraint`, `check_constraint_live`	Baskets and net edge under a stated relationship
Calculate fees and model values	`trading_fee`, `fair_value`	Fee estimates and parameterized model outputs
Try a paper entry	`paper_order`, `paper_portfolio`	Simulated fills and holdings in a local ledger

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.

All 13 MCP tools · Workflow examples

Reusable agent Skills

The repository includes six workflows that help agents move from individual tool calls to complete tasks:

Skill	Use it to
Data discovery	Find markets and organize research inputs
Constraint arbitrage	Examine related contracts and calculate fee-aware baskets
Quant strategy authoring	Develop strategies with Oracle3's Python interfaces
Agent strategy authoring	Build agent-driven decision workflows
Paper operations	Run and inspect simulated trading sessions
Live operations	Configure and operate the separate live engine

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.

Build with Python and the CLI

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

Component	Build with it
MCP server	Structured market access and local paper operations for agents
Relations and fees	Shared calculations for related-contract strategies
Strategies	Logical-relation, statistical, and model-driven approaches
Trading engine	Paper/live adapters for Kalshi, Polymarket, and Solana via DFlow
Pricing	Wang-transform models and estimators for research
oracle3-extras	Optional venue, data-provider, and wallet integrations

CLI guide · Integration guide · Execution reference

Built for experimentation

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.

Project overview and Yicheng Yang's contributions · Fee-frontier study · Companion pricing research

Contribute

Help build better agent workflows, labeled evaluation tasks, strategy examples, and client integrations. New venue, data-provider, and wallet integrations start in oracle3-extras.

Contributing guide · Open problems · Discussions

Origin and attribution

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.

License

Apache 2.0; see LICENSE. Original U Lab and Coinjure portions retain their MIT license in NOTICE.

This software is for research and education. Trading involves financial risk.