Polymarket MCP Server

Skill by ara.so — MCP Skills collection.

AI-powered MCP server that enables Claude to autonomously trade, analyze, and manage positions on Polymarket prediction markets. Provides 45 comprehensive tools across market discovery, analysis, trading, portfolio management, and real-time monitoring with WebSocket support.

由ara.so开发的Skill — MCP Skills合集。

一款AI驱动的MCP服务器，可让Claude在Polymarket预测市场中自主进行交易、分析和持仓管理。提供涵盖市场发现、分析、交易、投资组合管理以及基于WebSocket的实时监控在内的45款全面工具。

Installation

安装

Quick Start (DEMO Mode - No Wallet Required)

快速开始（演示模式 - 无需钱包）

bash

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bash

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Clone and install

git clone https://github.com/caiovicentino/polymarket-mcp-server.git cd polymarket-mcp-server ./quickstart.sh

Or automated one-liner

curl -sSL https://raw.githubusercontent.com/caiovicentino/polymarket-mcp-server/main/quickstart.sh | bash

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curl -sSL https://raw.githubusercontent.com/caiovicentino/polymarket-mcp-server/main/quickstart.sh | bash

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Full Installation (Trading Enabled)

完整安装（启用交易功能）

bash

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bash

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Use automated installer

./install.sh

Or manual installation

python -m venv venv source venv/bin/activate # Windows: venv\Scripts\activate pip install -e .

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python -m venv venv source venv/bin/activate # Windows: venv\Scripts\activate pip install -e .

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Configuration

配置

Create

.env

file:

bash

cp .env.example .env

DEMO Mode (Read-Only):

env

DEMO_MODE=true

Full Trading Mode:

env

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创建

.env

文件：

bash

cp .env.example .env

演示模式（只读）：

env

DEMO_MODE=true

完整交易模式：

env

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Required

必填项

POLYGON_PRIVATE_KEY=${POLYGON_PRIVATE_KEY} POLYGON_ADDRESS=${POLYGON_ADDRESS}

Optional Safety Limits

可选安全限制

MAX_ORDER_SIZE_USD=1000 MAX_TOTAL_EXPOSURE_USD=5000 MAX_POSITION_SIZE_PER_MARKET=2000 MIN_LIQUIDITY_REQUIRED=10000 MAX_SPREAD_TOLERANCE=0.05 REQUIRE_CONFIRMATION_ABOVE_USD=500 ENABLE_AUTONOMOUS_TRADING=true

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MAX_ORDER_SIZE_USD=1000 MAX_TOTAL_EXPOSURE_USD=5000 MAX_POSITION_SIZE_PER_MARKET=2000 MIN_LIQUIDITY_REQUIRED=10000 MAX_SPREAD_TOLERANCE=0.05 REQUIRE_CONFIRMATION_ABOVE_USD=500 ENABLE_AUTONOMOUS_TRADING=true

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Claude Desktop Integration

Claude桌面端集成

Add to

claude_desktop_config.json

:

macOS:

~/Library/Application Support/Claude/claude_desktop_config.json

Windows:

%APPDATA%\Claude\claude_desktop_config.json

json

{
  "mcpServers": {
    "polymarket": {
      "command": "/absolute/path/to/venv/bin/python",
      "args": ["-m", "polymarket_mcp.server"],
      "cwd": "/absolute/path/to/polymarket-mcp-server",
      "env": {
        "POLYGON_PRIVATE_KEY": "${POLYGON_PRIVATE_KEY}",
        "POLYGON_ADDRESS": "${POLYGON_ADDRESS}"
      }
    }
  }
}

添加至

claude_desktop_config.json

：

macOS：

~/Library/Application Support/Claude/claude_desktop_config.json

Windows：

%APPDATA%\Claude\claude_desktop_config.json

json

{
  "mcpServers": {
    "polymarket": {
      "command": "/absolute/path/to/venv/bin/python",
      "args": ["-m", "polymarket_mcp.server"],
      "cwd": "/absolute/path/to/polymarket-mcp-server",
      "env": {
        "POLYGON_PRIVATE_KEY": "${POLYGON_PRIVATE_KEY}",
        "POLYGON_ADDRESS": "${POLYGON_ADDRESS}"
      }
    }
  }
}

Tool Categories

工具分类

1. Market Discovery (8 Tools)

1. 市场发现（8款工具）

Search Markets

搜索市场

python

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python

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Tool: search_markets

{ "query": "Trump election", "limit": 10, "offset": 0 }

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{ "query": "Trump election", "limit": 10, "offset": 0 }

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Get Trending Markets

获取热门市场

python

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python

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Tool: get_trending_markets

{ "period": "24h", # "24h", "7d", or "30d" "limit": 20 }

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{ "period": "24h", # "24h", "7d", or "30d" "limit": 20 }

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Markets Closing Soon

即将结束的市场

python

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python

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Tool: get_markets_closing_soon

{ "hours": 24, "limit": 10 }

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{ "hours": 24, "limit": 10 }

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Category-Specific Markets

特定分类市场

python

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python

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Tool: get_markets_by_category

{ "category": "Politics", # Politics, Sports, Crypto, Pop Culture "limit": 20 }

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{ "category": "Politics", # Politics, Sports, Crypto, Pop Culture "limit": 20 }

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2. Market Analysis (10 Tools)

2. 市场分析（10款工具）

Get Market Details

获取市场详情

python

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python

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Tool: get_market_details

{ "market_id": "0x1234..." }

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{ "market_id": "0x1234..." }

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Analyze Market Opportunity (AI-Powered)

AI驱动的市场机会分析

python

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python

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Tool: analyze_market_opportunity

{ "market_id": "0x1234...", "analysis_depth": "deep" # "quick" or "deep" }

Returns: BUY/SELL/HOLD recommendation with reasoning

返回：包含理由的买入/卖出/持有建议

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Get Orderbook Depth

获取订单簿深度

python

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python

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Tool: get_orderbook_depth

{ "token_id": "0x5678..." }

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{ "token_id": "0x5678..." }

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Compare Markets

对比市场

python

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python

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Tool: compare_markets

{ "market_ids": ["0x1234...", "0x5678...", "0x9abc..."] }

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{ "market_ids": ["0x1234...", "0x5678...", "0x9abc..."] }

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Calculate Spread

计算点差

python

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python

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Tool: calculate_spread

{ "token_id": "0x5678..." }

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{ "token_id": "0x5678..." }

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3. Trading (12 Tools)

3. 交易功能（12款工具）

Place Limit Order

挂限价单

python

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python

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Tool: place_limit_order

{ "token_id": "0x5678...", "side": "BUY", # or "SELL" "price": 0.65, "size": 100, "time_in_force": "GTC" # GTC, GTD, FOK, FAK }

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{ "token_id": "0x5678...", "side": "BUY", # or "SELL" "price": 0.65, "size": 100, "time_in_force": "GTC" # GTC, GTD, FOK, FAK }

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Place Market Order

挂市价单

python

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python

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Tool: place_market_order

{ "token_id": "0x5678...", "side": "BUY", "amount": 50 }

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{ "token_id": "0x5678...", "side": "BUY", "amount": 50 }

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Smart Trade (AI-Powered)

AI智能交易

python

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python

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Tool: smart_trade

{ "instruction": "Buy $200 worth of Yes on Trump 2024 election if price below 0.60", "strategy": "passive" # aggressive, passive, or mid }

Automatically parses instruction and executes optimal trade

自动解析指令并执行最优交易

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Get AI Suggested Prices

获取AI建议价格

python

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Tool: get_ai_suggested_prices

{ "token_id": "0x5678...", "side": "BUY", "strategy": "mid" # aggressive, passive, or mid }

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{ "token_id": "0x5678...", "side": "BUY", "strategy": "mid" # aggressive, passive, or mid }

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Cancel Order

取消订单

python

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python

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Tool: cancel_order

{ "order_id": "0xabcd..." }

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{ "order_id": "0xabcd..." }

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Get Open Orders

获取未成交订单

python

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python

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Tool: get_open_orders

{ "market_id": "0x1234..." # Optional filter }

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{ "market_id": "0x1234..." # 可选过滤条件 }

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4. Portfolio Management (8 Tools)

4. 投资组合管理（8款工具）

Get Portfolio Summary

获取投资组合摘要

python

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Tool: get_portfolio_summary

{}

Returns: total value, positions, P&L, risk metrics

返回：总价值、持仓、盈亏、风险指标

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Get Active Positions

获取活跃持仓

python

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python

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Tool: get_active_positions

{ "market_id": "0x1234..." # Optional filter }

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{ "market_id": "0x1234..." # 可选过滤条件 }

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Calculate P&L

计算盈亏

python

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Tool: calculate_pnl

{ "position_id": "0x5678...", "include_unrealized": true }

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{ "position_id": "0x5678...", "include_unrealized": true }

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Analyze Portfolio Risk

分析投资组合风险

python

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python

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Tool: analyze_portfolio_risk

{}

Returns: concentration risk, liquidity risk, diversification score

返回：集中度风险、流动性风险、分散度评分

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Optimize Portfolio (AI-Powered)

AI驱动的投资组合优化

python

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Tool: optimize_portfolio

{ "risk_profile": "balanced", # conservative, balanced, or aggressive "target_allocation": { "Politics": 0.4, "Sports": 0.3, "Crypto": 0.3 } }

Returns: rebalancing suggestions with rationale

返回：包含理由的再平衡建议

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Get Trade History

获取交易历史

python

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python

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Tool: get_trade_history

{ "start_date": "2024-01-01", "end_date": "2024-12-31", "market_id": "0x1234..." # Optional }

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{ "start_date": "2024-01-01", "end_date": "2024-12-31", "market_id": "0x1234..." # 可选过滤条件 }

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5. Real-Time Monitoring (7 Tools)

5. 实时监控（7款工具）

Subscribe to Market Updates

订阅市场更新

python

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python

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Tool: subscribe_to_market

{ "market_id": "0x1234...", "channels": ["price", "orderbook", "trades"] }

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{ "market_id": "0x1234...", "channels": ["price", "orderbook", "trades"] }

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Subscribe to User Orders

订阅用户订单状态

python

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python

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Tool: subscribe_to_user_orders

{}

Monitors all your order status changes

监控所有订单状态变化

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Get WebSocket Status

获取WebSocket状态

python

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python

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Tool: get_websocket_status

{}

Returns: connection state, subscriptions, message counts

返回：连接状态、订阅列表、消息计数

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Unsubscribe from Market

取消市场订阅

python

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python

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Tool: unsubscribe_from_market

{ "market_id": "0x1234..." }

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{ "market_id": "0x1234..." }

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Python API Usage

Python API 使用

Direct Python Integration

直接Python集成

python

from polymarket_mcp.client import PolymarketClient
from decimal import Decimal
import os

python

from polymarket_mcp.client import PolymarketClient
from decimal import Decimal
import os

Initialize client

client = PolymarketClient( private_key=os.getenv("POLYGON_PRIVATE_KEY"), polygon_address=os.getenv("POLYGON_ADDRESS") )

Search markets

markets = client.search_markets(query="election", limit=5) for market in markets: print(f"{market['question']} - Volume: ${market['volume24h']}")

Get market analysis

market_id = markets[0]['id'] analysis = client.analyze_market_opportunity( market_id=market_id, analysis_depth="deep" ) print(f"Recommendation: {analysis['recommendation']}") print(f"Reasoning: {analysis['reasoning']}")

Place order with safety checks

token_id = markets[0]['tokens'][0]['id'] order = client.place_limit_order( token_id=token_id, side="BUY", price=Decimal("0.55"), size=100, time_in_force="GTC" ) print(f"Order placed: {order['id']}")

Monitor portfolio

portfolio = client.get_portfolio_summary() print(f"Total Value: ${portfolio['total_value_usd']}") print(f"Unrealized P&L: ${portfolio['unrealized_pnl']}")

Get real-time updates via WebSocket

client.subscribe_to_market( market_id=market_id, channels=["price", "orderbook"] )

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client.subscribe_to_market( market_id=market_id, channels=["price", "orderbook"] )

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Trading Strategies Example

交易策略示例

python

from polymarket_mcp.client import PolymarketClient
from decimal import Decimal

class SimpleArbitrageBot:
    def __init__(self, client: PolymarketClient):
        self.client = client
        self.min_spread = Decimal("0.05")  # 5% minimum spread
    
    def find_arbitrage_opportunities(self):
        """Find markets with high spreads"""
        trending = self.client.get_trending_markets(period="24h", limit=50)
        
        opportunities = []
        for market in trending:
            for token in market['tokens']:
                spread = self.client.calculate_spread(token['id'])
                
                if spread['spread_percentage'] > float(self.min_spread):
                    opportunities.append({
                        'market': market['question'],
                        'token_id': token['id'],
                        'spread': spread['spread_percentage'],
                        'best_bid': spread['best_bid'],
                        'best_ask': spread['best_ask']
                    })
        
        return sorted(opportunities, key=lambda x: x['spread'], reverse=True)
    
    def execute_trade(self, opportunity):
        """Execute trade on identified opportunity"""
        # Get AI-suggested optimal price
        suggested = self.client.get_ai_suggested_prices(
            token_id=opportunity['token_id'],
            side="BUY",
            strategy="mid"
        )
        
        # Place order
        order = self.client.place_limit_order(
            token_id=opportunity['token_id'],
            side="BUY",
            price=Decimal(str(suggested['suggested_price'])),
            size=100,
            time_in_force="GTC"
        )
        
        return order

python

from polymarket_mcp.client import PolymarketClient
from decimal import Decimal

class SimpleArbitrageBot:
    def __init__(self, client: PolymarketClient):
        self.client = client
        self.min_spread = Decimal("0.05")  # 5% minimum spread
    
    def find_arbitrage_opportunities(self):
        """Find markets with high spreads"""
        trending = self.client.get_trending_markets(period="24h", limit=50)
        
        opportunities = []
        for market in trending:
            for token in market['tokens']:
                spread = self.client.calculate_spread(token['id'])
                
                if spread['spread_percentage'] > float(self.min_spread):
                    opportunities.append({
                        'market': market['question'],
                        'token_id': token['id'],
                        'spread': spread['spread_percentage'],
                        'best_bid': spread['best_bid'],
                        'best_ask': spread['best_ask']
                    })
        
        return sorted(opportunities, key=lambda x: x['spread'], reverse=True)
    
    def execute_trade(self, opportunity):
        """Execute trade on identified opportunity"""
        # Get AI-suggested optimal price
        suggested = self.client.get_ai_suggested_prices(
            token_id=opportunity['token_id'],
            side="BUY",
            strategy="mid"
        )
        
        # Place order
        order = self.client.place_limit_order(
            token_id=opportunity['token_id'],
            side="BUY",
            price=Decimal(str(suggested['suggested_price'])),
            size=100,
            time_in_force="GTC"
        )
        
        return order

Usage

client = PolymarketClient( private_key=os.getenv("POLYGON_PRIVATE_KEY"), polygon_address=os.getenv("POLYGON_ADDRESS") )

bot = SimpleArbitrageBot(client) opportunities = bot.find_arbitrage_opportunities()

for opp in opportunities[:3]: print(f"Opportunity: {opp['market']}") print(f"Spread: {opp['spread']:.2%}") order = bot.execute_trade(opp) print(f"Order placed: {order['id']}\n")

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client = PolymarketClient( private_key=os.getenv("POLYGON_PRIVATE_KEY"), polygon_address=os.getenv("POLYGON_ADDRESS") )

bot = SimpleArbitrageBot(client) opportunities = bot.find_arbitrage_opportunities()

for opp in opportunities[:3]: print(f"Opportunity: {opp['market']}") print(f"Spread: {opp['spread']:.2%}") order = bot.execute_trade(opp) print(f"Order placed: {order['id']}\n")

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Risk Management Example

风险管理示例

python

from polymarket_mcp.client import PolymarketClient

class RiskManager:
    def __init__(self, client: PolymarketClient):
        self.client = client
        self.max_exposure_usd = 5000
        self.max_position_size = 2000
        self.max_category_allocation = 0.4  # 40%
    
    def check_trade_allowed(self, trade_value_usd: float, market_category: str) -> dict:
        """Validate if trade passes risk checks"""
        portfolio = self.client.get_portfolio_summary()
        risk_analysis = self.client.analyze_portfolio_risk()
        
        # Check total exposure
        new_exposure = portfolio['total_value_usd'] + trade_value_usd
        if new_exposure > self.max_exposure_usd:
            return {
                "allowed": False,
                "reason": f"Would exceed max exposure: ${new_exposure} > ${self.max_exposure_usd}"
            }
        
        # Check position size
        if trade_value_usd > self.max_position_size:
            return {
                "allowed": False,
                "reason": f"Trade size ${trade_value_usd} exceeds max position ${self.max_position_size}"
            }
        
        # Check category concentration
        category_exposure = risk_analysis.get('category_concentration', {}).get(market_category, 0)
        new_category_allocation = (category_exposure + trade_value_usd) / new_exposure
        
        if new_category_allocation > self.max_category_allocation:
            return {
                "allowed": False,
                "reason": f"Would exceed {market_category} allocation: {new_category_allocation:.1%}"
            }
        
        return {"allowed": True, "reason": "All risk checks passed"}
    
    def suggest_position_size(self, market_id: str) -> float:
        """Suggest optimal position size based on risk"""
        portfolio = self.client.get_portfolio_summary()
        market = self.client.get_market_details(market_id)
        
        # Kelly Criterion simplified
        win_rate = 0.55  # Estimate from market analysis
        odds = market['tokens'][0]['price']
        
        kelly_fraction = (win_rate - (1 - win_rate) / odds) / odds
        suggested_size = portfolio['total_value_usd'] * kelly_fraction * 0.5  # Half Kelly
        
        return min(suggested_size, self.max_position_size)

python

from polymarket_mcp.client import PolymarketClient

class RiskManager:
    def __init__(self, client: PolymarketClient):
        self.client = client
        self.max_exposure_usd = 5000
        self.max_position_size = 2000
        self.max_category_allocation = 0.4  # 40%
    
    def check_trade_allowed(self, trade_value_usd: float, market_category: str) -> dict:
        """Validate if trade passes risk checks"""
        portfolio = self.client.get_portfolio_summary()
        risk_analysis = self.client.analyze_portfolio_risk()
        
        # Check total exposure
        new_exposure = portfolio['total_value_usd'] + trade_value_usd
        if new_exposure > self.max_exposure_usd:
            return {
                "allowed": False,
                "reason": f"Would exceed max exposure: ${new_exposure} > ${self.max_exposure_usd}"
            }
        
        # Check position size
        if trade_value_usd > self.max_position_size:
            return {
                "allowed": False,
                "reason": f"Trade size ${trade_value_usd} exceeds max position ${self.max_position_size}"
            }
        
        # Check category concentration
        category_exposure = risk_analysis.get('category_concentration', {}).get(market_category, 0)
        new_category_allocation = (category_exposure + trade_value_usd) / new_exposure
        
        if new_category_allocation > self.max_category_allocation:
            return {
                "allowed": False,
                "reason": f"Would exceed {market_category} allocation: {new_category_allocation:.1%}"
            }
        
        return {"allowed": True, "reason": "All risk checks passed"}
    
    def suggest_position_size(self, market_id: str) -> float:
        """Suggest optimal position size based on risk"""
        portfolio = self.client.get_portfolio_summary()
        market = self.client.get_market_details(market_id)
        
        # Kelly Criterion simplified
        win_rate = 0.55  # Estimate from market analysis
        odds = market['tokens'][0]['price']
        
        kelly_fraction = (win_rate - (1 - win_rate) / odds) / odds
        suggested_size = portfolio['total_value_usd'] * kelly_fraction * 0.5  # Half Kelly
        
        return min(suggested_size, self.max_position_size)

Usage

client = PolymarketClient( private_key=os.getenv("POLYGON_PRIVATE_KEY"), polygon_address=os.getenv("POLYGON_ADDRESS") )

risk_mgr = RiskManager(client)

client = PolymarketClient( private_key=os.getenv("POLYGON_PRIVATE_KEY"), polygon_address=os.getenv("POLYGON_ADDRESS") )

risk_mgr = RiskManager(client)

Check if trade is allowed

trade_check = risk_mgr.check_trade_allowed( trade_value_usd=500, market_category="Politics" )

if trade_check['allowed']: suggested_size = risk_mgr.suggest_position_size(market_id="0x1234...") print(f"Suggested position size: ${suggested_size:.2f}") else: print(f"Trade blocked: {trade_check['reason']}")

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trade_check = risk_mgr.check_trade_allowed( trade_value_usd=500, market_category="Politics" )

if trade_check['allowed']: suggested_size = risk_mgr.suggest_position_size(market_id="0x1234...") print(f"Suggested position size: ${suggested_size:.2f}") else: print(f"Trade blocked: {trade_check['reason']}")

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Common Patterns

常见模式

Pattern 1: Market Research Pipeline

模式1：市场研究流程

python

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1. Find trending markets

1. 查找热门市场

trending = client.get_trending_markets(period="24h", limit=20)

2. Analyze each market

2. 分析每个市场

for market in trending[:5]: analysis = client.analyze_market_opportunity( market_id=market['id'], analysis_depth="deep" )

if analysis['recommendation'] == 'BUY':
    # 3. Get orderbook depth
    orderbook = client.get_orderbook_depth(market['tokens'][0]['id'])
    
    # 4. Check liquidity
    if orderbook['total_liquidity'] > 10000:
        print(f"Good opportunity: {market['question']}")
        print(f"Reason: {analysis['reasoning']}")

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for market in trending[:5]: analysis = client.analyze_market_opportunity( market_id=market['id'], analysis_depth="deep" )

if analysis['recommendation'] == 'BUY':
    # 3. 获取订单簿深度
    orderbook = client.get_orderbook_depth(market['tokens'][0]['id'])
    
    # 4. 检查流动性
    if orderbook['total_liquidity'] > 10000:
        print(f"Good opportunity: {market['question']}")
        print(f"Reason: {analysis['reasoning']}")

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Pattern 2: Automated Portfolio Rebalancing

模式2：自动化投资组合再平衡

python

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1. Get portfolio risk analysis

1. 获取投资组合风险分析

risk = client.analyze_portfolio_risk()

2. If risk is high, get optimization suggestions

2. 如果风险较高，获取优化建议

if risk['overall_risk_score'] > 70: optimization = client.optimize_portfolio( risk_profile="balanced", target_allocation={ "Politics": 0.35, "Sports": 0.30, "Crypto": 0.35 } )

# 3. Execute suggested trades
for suggestion in optimization['suggestions']:
    if suggestion['action'] == 'REDUCE':
        client.place_market_order(
            token_id=suggestion['token_id'],
            side="SELL",
            amount=suggestion['amount']
        )

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if risk['overall_risk_score'] > 70: optimization = client.optimize_portfolio( risk_profile="balanced", target_allocation={ "Politics": 0.35, "Sports": 0.30, "Crypto": 0.35 } )

# 3. 执行建议交易
for suggestion in optimization['suggestions']:
    if suggestion['action'] == 'REDUCE':
        client.place_market_order(
            token_id=suggestion['token_id'],
            side="SELL",
            amount=suggestion['amount']
        )

undefined

Pattern 3: Real-Time Monitoring with Alerts

模式3：带告警的实时监控

python

import asyncio

async def monitor_positions():
    # Subscribe to user orders
    client.subscribe_to_user_orders()
    
    # Monitor portfolio value
    while True:
        portfolio = client.get_portfolio_summary()
        
        # Alert on significant P&L change
        if abs(portfolio['unrealized_pnl']) > 500:
            print(f"⚠️ Large P&L movement: ${portfolio['unrealized_pnl']}")
            
            # Analyze if should close positions
            risk = client.analyze_portfolio_risk()
            if risk['liquidity_risk'] > 80:
                print("🚨 High liquidity risk - consider closing positions")
        
        await asyncio.sleep(60)  # Check every minute

python

import asyncio

async def monitor_positions():
    # Subscribe to user orders
    client.subscribe_to_user_orders()
    
    # Monitor portfolio value
    while True:
        portfolio = client.get_portfolio_summary()
        
        # Alert on significant P&L change
        if abs(portfolio['unrealized_pnl']) > 500:
            print(f"⚠️ Large P&L movement: ${portfolio['unrealized_pnl']}")
            
            # Analyze if should close positions
            risk = client.analyze_portfolio_risk()
            if risk['liquidity_risk'] > 80:
                print("🚨 High liquidity risk - consider closing positions")
        
        await asyncio.sleep(60)  # Check every minute

Run monitoring

asyncio.run(monitor_positions())

undefined

asyncio.run(monitor_positions())

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Web Dashboard

Web控制台

Start the visual web interface:

bash

polymarket-web

启动可视化Web界面：

bash

polymarket-web

Or: ./start_web_dashboard.sh

Access at: http://localhost:8080

访问地址：http://localhost:8080


Dashboard features:
- Real-time market monitoring
- Configuration management with visual controls
- AI-powered market analysis
- System statistics and performance charts
- Live WebSocket status


控制台功能：
- 实时市场监控
- 可视化配置管理
- AI驱动的市场分析
- 系统统计与性能图表
- 实时WebSocket状态

Configuration Options

配置选项

Safety Limits

安全限制

env

undefined

env

undefined

Order Limits

订单限制

MAX_ORDER_SIZE_USD=1000 MAX_POSITION_SIZE_PER_MARKET=2000 MAX_TOTAL_EXPOSURE_USD=5000

Liquidity Checks

流动性检查

MIN_LIQUIDITY_REQUIRED=10000 MAX_SPREAD_TOLERANCE=0.05

Confirmations

确认机制

REQUIRE_CONFIRMATION_ABOVE_USD=500 ENABLE_AUTONOMOUS_TRADING=true

undefined

REQUIRE_CONFIRMATION_ABOVE_USD=500 ENABLE_AUTONOMOUS_TRADING=true

undefined

Rate Limiting

速率限制

env

undefined

env

undefined

API Rate Limits (per minute)

API速率限制（每分钟）

RATE_LIMIT_READ_PER_MIN=100 RATE_LIMIT_WRITE_PER_MIN=20 RATE_LIMIT_BURST_SIZE=5

undefined

RATE_LIMIT_READ_PER_MIN=100 RATE_LIMIT_WRITE_PER_MIN=20 RATE_LIMIT_BURST_SIZE=5

undefined

WebSocket Configuration

WebSocket配置

env

WEBSOCKET_RECONNECT_DELAY=5
WEBSOCKET_MAX_RETRIES=10
WEBSOCKET_PING_INTERVAL=30

env

WEBSOCKET_RECONNECT_DELAY=5
WEBSOCKET_MAX_RETRIES=10
WEBSOCKET_PING_INTERVAL=30

Troubleshooting

故障排查

Connection Issues

连接问题

bash

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bash

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Test API connectivity

测试API连通性

python -m polymarket_mcp.test_connection

Check WebSocket status

检查WebSocket状态

python -c "from polymarket_mcp.client import PolymarketClient;
c = PolymarketClient();
print(c.get_websocket_status())"

undefined

python -c "from polymarket_mcp.client import PolymarketClient;
c = PolymarketClient();
print(c.get_websocket_status())"

undefined

Authentication Errors

认证错误

python

undefined

python

undefined

Verify wallet configuration

验证钱包配置

from polymarket_mcp.client import PolymarketClient import os

client = PolymarketClient( private_key=os.getenv("POLYGON_PRIVATE_KEY"), polygon_address=os.getenv("POLYGON_ADDRESS") )

from polymarket_mcp.client import PolymarketClient import os

client = PolymarketClient( private_key=os.getenv("POLYGON_PRIVATE_KEY"), polygon_address=os.getenv("POLYGON_ADDRESS") )

Test authentication

测试认证

try: portfolio = client.get_portfolio_summary() print("✓ Authentication successful") except Exception as e: print(f"✗ Auth failed: {e}")

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try: portfolio = client.get_portfolio_summary() print("✓ Authentication successful") except Exception as e: print(f"✗ Auth failed: {e}")

undefined

Rate Limit Handling

速率限制处理

python

from polymarket_mcp.client import PolymarketClient
from time import sleep

client = PolymarketClient()

python

from polymarket_mcp.client import PolymarketClient
from time import sleep

client = PolymarketClient()

Built-in rate limiting

for i in range(100): try: markets = client.search_markets(query=f"test {i}", limit=1) # Rate limiter automatically throttles requests except Exception as e: if "rate limit" in str(e).lower(): print("Rate limit hit, waiting...") sleep(60)

undefined

for i in range(100): try: markets = client.search_markets(query=f"test {i}", limit=1) # Rate limiter automatically throttles requests except Exception as e: if "rate limit" in str(e).lower(): print("Rate limit hit, waiting...") sleep(60)

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Order Validation Failures

订单验证失败

python

undefined

python

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Check order validation before placing

下单前检查订单有效性

from polymarket_mcp.client import PolymarketClient from decimal import Decimal

client = PolymarketClient()

from polymarket_mcp.client import PolymarketClient from decimal import Decimal

client = PolymarketClient()

Validate market liquidity

orderbook = client.get_orderbook_depth(token_id="0x5678...") if orderbook['total_liquidity'] < 10000: print("⚠️ Low liquidity - consider smaller order size")

Calculate expected slippage

spread = client.calculate_spread(token_id="0x5678...") if spread['spread_percentage'] > 0.05: print(f"⚠️ High spread: {spread['spread_percentage']:.2%}")

undefined

spread = client.calculate_spread(token_id="0x5678...") if spread['spread_percentage'] > 0.05: print(f"⚠️ High spread: {spread['spread_percentage']:.2%}")

undefined

DEMO Mode Limitations

演示模式限制

If you see "Trading disabled in DEMO mode":

bash

undefined

如果看到"Trading disabled in DEMO mode"：

bash

undefined

Switch to full mode

切换到完整模式

1. Get Polygon wallet private key

1. 获取Polygon钱包私钥

2. Update .env:

2. 更新.env：

DEMO_MODE=false POLYGON_PRIVATE_KEY=your_actual_key POLYGON_ADDRESS=0xYourAddress

3. Restart MCP server

3. 重启MCP服务器

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Testing

测试

bash

undefined

bash

undefined

Run all tests

运行所有测试

pytest

Test specific tool

测试特定工具

pytest tests/test_trading.py::test_place_limit_order

Test with real API (caution)

测试真实API（谨慎操作）

pytest tests/test_integration.py --run-live

undefined

pytest tests/test_integration.py --run-live

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Resources

资源

GitHub: https://github.com/caiovicentino/polymarket-mcp-server
Polymarket API Docs: https://docs.polymarket.com
MCP Protocol: https://modelcontextprotocol.io
Full Tool Reference: See
```
TOOLS_REFERENCE.md
```
in repository

GitHub：https://github.com/caiovicentino/polymarket-mcp-server
Polymarket API文档：https://docs.polymarket.com
MCP协议：https://modelcontextprotocol.io
完整工具参考：查看仓库中的
```
TOOLS_REFERENCE.md
```

polymarket-mcp-server

Original

Translation

Polymarket MCP Server

Polymarket MCP Server

Installation

安装

Quick Start (DEMO Mode - No Wallet Required)

快速开始（演示模式 - 无需钱包）

Clone and install

Clone and install

Or automated one-liner

Or automated one-liner

Full Installation (Trading Enabled)

完整安装（启用交易功能）

Use automated installer

Use automated installer

Or manual installation

Or manual installation

Configuration

配置

Required

必填项

Optional Safety Limits

可选安全限制

Claude Desktop Integration

Claude桌面端集成

Tool Categories

工具分类

1. Market Discovery (8 Tools)

1. 市场发现（8款工具）

Search Markets

搜索市场

Tool: search_markets

Tool: search_markets

Get Trending Markets

获取热门市场

Tool: get_trending_markets

Tool: get_trending_markets

Markets Closing Soon

即将结束的市场

Tool: get_markets_closing_soon

Tool: get_markets_closing_soon

Category-Specific Markets

特定分类市场

Tool: get_markets_by_category

Tool: get_markets_by_category

2. Market Analysis (10 Tools)

2. 市场分析（10款工具）

Get Market Details

获取市场详情

Tool: get_market_details

Tool: get_market_details

Analyze Market Opportunity (AI-Powered)

AI驱动的市场机会分析

Tool: analyze_market_opportunity

Tool: analyze_market_opportunity

Returns: BUY/SELL/HOLD recommendation with reasoning

返回：包含理由的买入/卖出/持有建议

Get Orderbook Depth

获取订单簿深度

Tool: get_orderbook_depth

Tool: get_orderbook_depth

Compare Markets

对比市场

Tool: compare_markets

Tool: compare_markets

Calculate Spread

计算点差

Tool: calculate_spread

Tool: calculate_spread

3. Trading (12 Tools)

3. 交易功能（12款工具）

Place Limit Order

挂限价单

Tool: place_limit_order

Tool: place_limit_order

Place Market Order

挂市价单

Tool: place_market_order