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PolyMarket MCP Server: Seamless APIs & Lightning-Fast Data - MCP Implementation

PolyMarket MCP Server: Seamless APIs & Lightning-Fast Data

Empower real-time trading mastery with PolyMarket’s MCP Server: seamless API integration, lightning-fast data processing, and ironclad reliability for pros who demand precision.

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About PolyMarket MCP Server

What is PolyMarket MCP Server: Seamless APIs & Lightning-Fast Data?

PolyMarket MCP Server is a purpose-built API gateway designed to streamline access to real-time and historical market data from decentralized prediction markets. By abstracting complex data retrieval processes, this server enables developers to integrate market insights into applications with minimal effort while maintaining high performance and reliability.

How to Use PolyMarket MCP Server: Seamless APIs & Lightning-Fast Data?

Implementation follows three core steps:

  1. Setup Configuration: Install required dependencies and configure API credentials via environment variables
  2. API Interaction: Leverage RESTful endpoints to query market states, price histories, and outcome probabilities
  3. Error Handling: Implement retry logic using server-provided error codes for transient failures

Full integration guides and SDK samples are available in the official repository.

PolyMarket MCP Server Features

Key Features of PolyMarket MCP Server: Seamless APIs & Lightning-Fast Data?

  • Real-time market state updates with sub-second latency guarantees
  • Granular historical data slicing via customizable timeframes (1d/7d/30d/all)
  • Automated rate limiting with adaptive backoff strategies
  • Comprehensive error categorization (security, network, validation)
  • Market outcome probability normalization engine

Use Cases of PolyMarket MCP Server: Seamless APIs & Lightning-Fast Data?

Common applications include:

Financial Analysis

Power algorithmic trading strategies with prediction market sentiment

Academic Research

Aggregate event probability trends for behavioral economics studies

Decision Support Systems

Integrate market-derived probabilities into corporate risk models

Decentralized Applications

Provide trustless market data feeds for blockchain-based platforms

PolyMarket MCP Server FAQ

FAQ from PolyMarket MCP Server: Seamless APIs & Lightning-Fast Data?

What Python versions are supported?

Requires Python 3.9+ to leverage modern async capabilities and type hinting features

How are API limits enforced?

Uses exponential backoff with per-second request throttling and 429 response codes

Can historical data be backfilled?

Market creation timestamps define data availability - historical queries cannot precede market launch

What security measures are included?

JWT token validation, IP whitelisting, and rate limiting protect against unauthorized access

Content

PolyMarket MCP Server

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A Model Context Protocol (MCP) server that provides access to prediction market data through the PolyMarket API. This server implements a standardized interface for retrieving market information, prices, and historical data from prediction markets.

Features

  • Real-time prediction market data with current prices and probabilities
  • Detailed market information including categories, resolution dates, and descriptions
  • Historical price and volume data with customizable timeframes (1d, 7d, 30d, all)
  • Built-in error handling and rate limit management
  • Clean data formatting for easy consumption

Installation

Installing via Smithery

To install PolyMarket Predictions for Claude Desktop automatically via Smithery:

npx -y @smithery/cli install polymarket_mcp --client claude

Claude Desktop

  • On MacOS: ~/Library/Application\ Support/Claude/claude_desktop_config.json
  • On Windows: %APPDATA%/Claude/claude_desktop_config.json
Development/Unpublished Servers Configuration
    "mcpServers": {
        "polymarket-mcp": {
            "command": "uv",
            "args": [
            "--directory",
            "/Users/{INSERT_USER}/YOUR/PATH/TO/polymarket-mcp",
            "run",
            "polymarket-mcp" //or src/polymarket_mcp/server.py
            ],
            "env": {
                "KEY": "<insert poly market api key>",
                "FUNDER": "<insert polymarket wallet address"
            }
        }
    }

Running Locally

  1. Clone the repository and install dependencies:

Install Libraries

uv pip install -e .

Running

After connecting Claude client with the MCP tool via json file and installing the packages, Claude should see the server's mcp tools:

You can run the sever yourself via: In polymarket-mcp repo:

uv run src/polymarket_mcp/server.py

*if you want to run the server inspector along with the server:

npx @modelcontextprotocol/inspector uv --directory C:\\Users\\{INSERT_USER}\\YOUR\\PATH\\TO\\polymarket-mcp run src/polymarket_mcp/server.py
  1. Create a .env file with your PolyMarket API key:
Key=your_api_key_here
Funder=poly market wallet address

After connecting Claude client with the MCP tool via json file, run the server: In alpha-vantage-mcp repo: uv run src/polymarket_mcp/server.py

Available Tools

The server implements four tools:

  • get-market-info: Get detailed information about a specific prediction market
  • list-markets: List available prediction markets with filtering options
  • get-market-prices: Get current prices and trading information
  • get-market-history: Get historical price and volume data

get-market-info

Input Schema:

{
    "market_id": {
        "type": "string",
        "description": "Market ID or slug"
    }
}

Example Response:

Title: Example Market
Category: Politics
Status: Open
Resolution Date: 2024-12-31
Volume: $1,234,567.89
Liquidity: $98,765.43
Description: This is an example prediction market...
---

list-markets

Input Schema:

{
    "status": {
        "type": "string",
        "description": "Filter by market status",
        "enum": ["open", "closed", "resolved"]
    },
    "limit": {
        "type": "integer",
        "description": "Number of markets to return",
        "default": 10,
        "minimum": 1,
        "maximum": 100
    },
    "offset": {
        "type": "integer",
        "description": "Number of markets to skip (for pagination)",
        "default": 0,
        "minimum": 0
    }
}

Example Response:

Available Markets:

ID: market-123
Title: US Presidential Election 2024
Status: Open
Volume: $1,234,567.89
---

ID: market-124
Title: Oscar Best Picture 2024
Status: Open
Volume: $234,567.89
---

get-market-prices

Input Schema:

{
    "market_id": {
        "type": "string",
        "description": "Market ID or slug"
    }
}

Example Response:

Current Market Prices for US Presidential Election 2024

Outcome: Democratic
Price: $0.6500
Probability: 65.0%
---

Outcome: Republican
Price: $0.3500
Probability: 35.0%
---

get-market-history

Input Schema:

{
    "market_id": {
        "type": "string",
        "description": "Market ID or slug"
    },
    "timeframe": {
        "type": "string",
        "description": "Time period for historical data",
        "enum": ["1d", "7d", "30d", "all"],
        "default": "7d"
    }
}

Example Response:

Historical Data for US Presidential Election 2024
Time Period: 7d

Time: 2024-01-20T12:00:00Z
Price: $0.6500
Volume: $123,456.78
---

Time: 2024-01-19T12:00:00Z
Price: $0.6300
Volume: $98,765.43
---

Error Handling

The server includes comprehensive error handling for various scenarios:

  • Rate limiting (429 errors)
  • Invalid API keys (403 errors)
  • Invalid market IDs (404 errors)
  • Network connectivity issues
  • API timeout conditions (30-second timeout)
  • Malformed responses

Error messages are returned in a clear, human-readable format.

Prerequisites

  • Python 3.9 or higher
  • httpx>=0.24.0
  • mcp-core
  • python-dotenv>=1.0.0

Contributing

Contributions are welcome! Please feel free to submit a Pull Request. For major changes, please open an issue first to discuss what you would like to change.

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