CrowdCent MCP Server

Name: CrowdCent MCP Server
Author: crowdcent

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.

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

Clone this repository:

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

(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)