Content
# 🤖 Polymarket MCP Server: Trade Prediction Markets with AI Agents
Connect Polymarket to your AI. The official-like MCP implementation for real-time prediction market analysis, automated odds tracking, and AI-powered trading insights.
# 📊 Dashboard
<img width="1536" height="1024" alt="polymarket_mcp_banner" src="https://github.com/user-attachments/assets/95907a83-b0f9-4e11-b937-871420f33b17" />
<img width="1248" height="832" alt="polymarket_mcp_dashboard_v2" src="https://github.com/user-attachments/assets/cd654773-60f1-4b54-b4f1-0cd32b8438ed" />
# 🚀 Getting Started
```bash
import { Server } from "@modelcontextprotocol/sdk/server/index.js";
import { StdioServerTransport } from "@modelcontextprotocol/sdk/server/stdio.js";
import {
CallToolRequestSchema,
ListToolsRequestSchema,
} from "@modelcontextprotocol/sdk/types.js";
import axios from "axios";
const server = new Server(
{
name: "polymarket-mcp-server",
version: "1.0.0",
},
{
capabilities: {
tools: {},
},
}
);
/**
* Список доступных инструментов для ИИ
*/
server.setRequestHandler(ListToolsRequestSchema, async () => {
return {
tools: [
{
name: "search_markets",
description: "Search for active prediction markets on Polymarket by keyword",
inputSchema: {
type: "object",
properties: {
query: { type: "string", description: "Keyword to search (e.g., 'Bitcoin', 'Election')" },
},
required: ["query"],
},
},
{
name: "get_market_odds",
description: "Get real-time odds and order book data for a specific market ID",
inputSchema: {
type: "object",
properties: {
conditionId: { type: "string", description: "The unique condition ID of the market" },
},
required: ["conditionId"],
},
},
],
};
});
/**
* Логика выполнения инструментов
*/
server.setRequestHandler(CallToolRequestSchema, async (request) => {
const { name, arguments: args } = request.params;
try {
if (name === "search_markets") {
const query = args?.query as string;
const response = await axios.get(`${POLYMARKET_API_BASE}/markets?active=true`);
const markets = response.data
.filter((m: any) => m.question.toLowerCase().includes(query.toLowerCase()))
.slice(0, 5);
return {
content: [{ type: "text", text: JSON.stringify(markets, null, 2) }],
};
}
if (name === "get_market_odds") {
const conditionId = args?.conditionId as string;
const response = await axios.get(`${POLYMARKET_API_BASE}/book?token_id=${conditionId}`);
return {
content: [{ type: "text", text: JSON.stringify(response.data, null, 2) }],
};
}
throw new Error(`Tool not found: ${name}`);
} catch (error: any) {
return {
content: [{ type: "text", text: `Error: ${error.message}` }],
isError: true,
};
}
});
/**
* Запуск сервера через стандартный ввод/вывод (Stdio)
*/
async function main() {
const transport = new StdioServerTransport();
await server.connect(transport);
console.error("Polymarket MCP Server running on stdio");
}
main().catch((error) => {
console.error("Fatal error in main():", error);
process.exit(1);
});
```
---
# ✨ Features
* get_market_data: Fetch live odds for any Polymarket URL.
* search_markets: Find trending markets by keywords.
* execute_trade: (Optional/Advanced) Place orders via API.
# 🧱 Architecture
```bash
polymarket-mcp-server-ai-agents/
│
├── server/
│ ├── agents/
│ ├── strategies/
│ ├── api/
│ ├── data/
│ └── core/
│
├── scripts/
├── tests/
├── docs/
│
├── .env.example
├── README.md
├── package.json / requirements.txt
└── docker-compose.yml
```
* Agents – AI decision-makers
* Strategies – trading logic
* Data Layer – market + external signals
* API Layer – Polymarket integration
* Core Engine – execution + risk control
# Tech Stack
**Backend:**
* Python
* FastAPI
* Web3.py
**AI Layer:**
* OpenAI / LLMs
* Custom probability models
**Frontend:**
* React / Next.js
* TailwindCSS
* Recharts
**Data Layer:**
* WebSockets (real-time markets)
* PostgreSQL / Redis
Modern trading systems often use **event-driven architectures + real-time streams + AI scoring engines** ([Prolymarket][4])
# Use Cases
Automated prediction market trading
AI-powered crypto speculation
Political and event forecasting
Arbitrage strategies
Sentiment-based trading
## 🧪 Roadmap
- [ ] 🧱 Basic MCP server
Initial implementation of the MCP (Model Context Protocol) server core, including request handling and modular architecture.
- [ ] 🔗 Polymarket API integration
Connect to Polymarket APIs to fetch real-time market data, prices, and execute trades programmatically.
- [ ] 🤖 AI agent framework
Develop a flexible framework for creating and managing AI trading agents with pluggable strategies.
- [ ] 🧠 Strategy marketplace
Enable users to create, share, and deploy custom trading strategies in a modular marketplace.
- [ ] 📈 Reinforcement learning agents
Implement RL-based agents capable of learning and improving trading strategies over time.
- [ ] 📊 Dashboard UI
Build a web-based interface for monitoring agents, strategies, trades, and performance analytics.
# 🐳 6. Docker
### Dockerfile
```dockerfile
FROM python:3.11
WORKDIR /app
COPY . .
RUN pip install -r requirements.txt
CMD ["python", "main.py"]
```
Keywords: polymarket bot, AI trading agent, prediction market automation, crypto AI trading, decentralized trading bots, web3 AI tools
