# CrowdCent MCP Server
A Model Context Protocol (MCP) server that provides seamless integration with the [CrowdCent Challenge API](https://docs.crowdcent.com/), 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](https://github.com/astral-sh/uv) (Python package manager)
- CrowdCent API key (get one at [crowdcent.com](https://crowdcent.com))
## Installation
1. Clone this repository:
```bash
git clone https://github.com/crowdcent/crowdcent-mcp.git
cd crowdcent-mcp
```
2. (Optional) Install dependencies with uv:
```bash
uv venv
uv pip install -e .
```
## Configuration
### Setting up your API key
Create a `.env` file in the project root:
```bash
CROWDCENT_API_KEY=your_api_key_here
```
### Cursor Setup
Add the following to your Cursor settings (`~/.cursor/mcp.json` or through Cursor Settings UI):
```json
{
"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`
```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](https://docs.crowdcent.com/)
- [Hyperliquid Ranking Challenge](https://docs.crowdcent.com/hyperliquid-ranking/)
- [MCP Documentation](https://modelcontextprotocol.io/)
- [CrowdCent Challenge Python Client](https://pypi.org/project/crowdcent-challenge/)
## Support
For issues with:
- **This MCP server**: Open an issue in this repository
- **CrowdCent API**: Email info@crowdcent.com or join our Discord
