CrowdCent MCP Server

A Model Context Protocol (MCP) server that provides seamless integration with the CrowdCent Challenge API, enabling AI assistants to interact with CrowdCent's prediction challenges directly.

<div align="center"> <img src="/assets/startup.png" alt="MCP Server" /> </div>

Overview

This MCP server allows AI assistants like Claude Desktop and Cursor to:

• Access and manage CrowdCent challenges
• Download training and inference datasets
• Submit predictions
• Monitor submissions
• Access meta models

Prerequisites

• Python 3.12+
• uv (Python package manager)
• CrowdCent API key (get one at crowdcent.com)

Installation

1. Clone this repository:

git clone https://github.com/crowdcent/crowdcent-mcp.git
cd crowdcent-mcp

2. (Optional) Install dependencies with uv:

uv venv
uv pip install -e .

Configuration

Setting up your API key

Create a .env file in the project root:

CROWDCENT_API_KEY=your_api_key_here

Cursor Setup

Add the following to your Cursor settings (~/.cursor/mcp.json or through Cursor Settings UI):

{
  "mcpServers": {
    "crowdcent-mcp": {
      "command": "/path/to/.cargo/bin/uv",
      "args": ["run", 
        "--directory",
        "/path/to/crowdcent-mcp",
        "server.py"
      ]
    }
  }
}

Replace /path/to/ with your actual paths. For example:

• /home/username/.cargo/bin/uv on Linux
• /Users/username/.cargo/bin/uv on macOS
• C:\\Users\\username\\.cargo\\bin\\uv on Windows

Claude Desktop Setup

For Claude Desktop, add the following to your configuration file:

macOS: ~/Library/Application Support/Claude/claude_desktop_config.json
Windows: %APPDATA%\Claude\claude_desktop_config.json
Linux: ~/.config/Claude/claude_desktop_config.json

{
  "mcpServers": {
    "crowdcent-mcp": {
      "command": "uv",
      "args": ["run", 
        "--directory",
        "/path/to/crowdcent-mcp",
        "server.py"
      ]
    }
  }
}

Usage Examples

After configuring the MCP server in your AI assistant, you can use natural language to interact with CrowdCent:

"Download data, train a model, and submit predictions to the crowdcent challenge!"

"Download the crowdcent training data and do some EDA"

"Create time series folds for the crowdcent challenge and train/evaluate a model"

Troubleshooting

MCP server not connecting

• Ensure uv is installed and in your PATH
• Check that the directory path in your config is correct
• Verify the server.py file has execute permissions

API key issues

• Make sure your API key is valid
• Check if it's properly set in .env or passed to init_client

Submission errors

• Ensure your predictions file has the required columns: id, pred_10d, pred_30d
• Check that all asset IDs match the current inference period
• Verify submission window is still open (within 4 hours of inference data release)

Resources

• CrowdCent Documentation
• Hyperliquid Ranking Challenge
• MCP Documentation
• CrowdCent Challenge Python Client

Support

For issues with:

• This MCP server: Open an issue in this repository
• CrowdCent API: Email info@crowdcent.com or join our Discord