Oracle3 prediction-market arbitrage checks

Name: Oracle3 prediction-market arbitrage checks
Author: YichengYang-Ethan

by YichengYang-Ethanversion 1.2.2i★ –iRuns on your computer

Registry nameio.github.YichengYang-Ethan/oracle3

Install

Overview

Description from the maker (original English)

Kalshi and Polymarket data, venue fees, no-arbitrage checks and paper execution for AI agents.

PopNx MCP’s three pillars

These three are never merged into one score: opinions, measurements and popularity are different things.

User voiceiNo reviews yetReviews open together with accounts in a later step
Our basic checksi1 of 1 passedChecked from public information; code not scanned yet
UsageiNot measured yet★ –

What you can do with it

We have not written a plain-language summary for this one yet. Below are the facts we read from the registry.

Connection: Runs on your computer
Transport: stdio
Category: Crypto & blockchain

Install

Pick the AI app you use, then copy the settings.

Before you install

Basic checks: 1 of 1 passed
Runs on your computer with your user’s permissions (files, network, apps).
You fill in yourself: ORACLE3_MCP_LEDGER

You need

PyPIPython’s uv installed (for the uvx command)

Which app do you use?

ClaudeClaude CodeCursorVS CodeChatGPT

Pick your app above. We remember it, so next time the button says “Install in …”.

Open Claude Desktop → Settings → Developer → Edit Config, and paste this into claude_desktop_config.json

{
  "mcpServers": {
    "oracle3": {
      "command": "uvx",
      "args": [
        "oracle3",
        "mcp"
      ],
      "env": {
        "ORACLE3_MCP_LEDGER": "your-value"
      }
    }
  }
}

Run this in your terminal

claude mcp add oracle3 --env ORACLE3_MCP_LEDGER=your-value -- uvx oracle3 mcp

Fill in the empty values before running it.

Open in Cursor

Or copy the settings yourself

Paste into .cursor/mcp.json in your project, or ~/.cursor/mcp.json

{
  "mcpServers": {
    "oracle3": {
      "command": "uvx",
      "args": [
        "oracle3",
        "mcp"
      ],
      "env": {
        "ORACLE3_MCP_LEDGER": "your-value"
      }
    }
  }
}

Your browser asks to open the app, then the app shows the settings and asks you to confirm. Key values are left empty for you to fill in.

Open in VS Code

Or copy the settings yourself

Paste into .vscode/mcp.json in your project

{
  "servers": {
    "oracle3": {
      "type": "stdio",
      "command": "uvx",
      "args": [
        "oracle3",
        "mcp"
      ],
      "env": {
        "ORACLE3_MCP_LEDGER": "your-value"
      }
    }
  }
}

Your browser asks to open the app, then the app shows the settings and asks you to confirm. Key values are left empty for you to fill in.

This one runs on your computer, and ChatGPT can only connect to servers on the internet. Pick another app.

Your choice stays on this device in a cookie; no account needed.

Values you fill in

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

These settings are generated from the maker’s registry entry. If the README says otherwise, follow the README.

What it can do

Runs on your computer; the tool list appears in your AI app after you install it.

Basic checksi

We check information anyone can see; we have not read the code. Checks that do not apply are left out of the count. · Checked Nov 7, 2026

✓ Pass
Maker matches the repo
The registry name was verified with the GitHub account YichengYang-Ethan, which owns the repo
… Not checked yet
Still maintained
We have not read this repo from GitHub yet
… Not checked yet
Has a licence
We have not read this repo from GitHub yet
– Not applicable
Endpoint responds
This one runs on your computer; there is no internet endpoint
– Not applicable
Sign-in with OAuth
Runs on your computer and uses local settings instead
… Not checked yet
No hidden instructions in tool descriptions
Runs on your computer; we can only read tool lists from internet endpoints

Compared with others in Crypto & blockchaini

More stars than 0% of the other 1611 servers in this category
Published to the registry more recently than 56% of the other 1611 servers in this category
Basic checks equal to or better than 100% of the other 1611 servers in this category

Compared with every active server in this category in the registry. Stars count only when the publisher owns the repo.

Common questions

Is Oracle3 prediction-market arbitrage checks safe?

It passed 1 of 1 applicable basic checks. We have not read the code. Before adding a key or token, see what access the server asks for and use a key with only the permissions you need.

What keys or codes does it need?

The registry does not list a required key, though some servers ask you to sign in on first use.

Does it run on my computer or in the cloud?

It runs on your computer. Your AI app starts it when needed, and data does not pass through the maker’s servers (unless it calls an online service itself).

Is it still maintained?

The GitHub repo last changed –, and the latest version (1.2.2) reached the registry 38 days ago.

Which apps can use it?

Apps that support MCP, such as Claude Desktop, Claude Code, Cursor and VS Code. You also need Python (uv) on your computer.

What can I use instead?

In the same category: Crypto Derivatives Data: Perp Funding Rates, Open Interest, Basis, IV, Agent Data Tools: Prices and DeFi, Agent Data Tools: Chain data

Alternatives

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README from the maker

Open the README

Copied from the maker’s repo as written. PopNx MCP has not reviewed or endorsed it. HTML, images and scripts are removed. Links lead off this site.

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.*

Oracle3 prediction-market arbitrage checks

Overview

PopNx MCP’s three pillars

What you can do with it

Install

Before you install

Values you fill in

What it can do

Basic checksi

Maker matches the repo

Still maintained

Has a licence

Endpoint responds

Sign-in with OAuth

No hidden instructions in tool descriptions

Compared with others in Crypto & blockchaini

Common questions

Alternatives

Crypto Derivatives Data: Perp Funding Rates, Open Interest, Basis, IV

Agent Data Tools: Prices and DeFi

Agent Data Tools: Chain data

README from the maker

Oracle3

Why Oracle3

Quickstart

Give your agent a first task

What agents can do

Reusable agent Skills

Build with Python and the CLI

Built for experimentation

Contribute

Origin and attribution

License