> ## Documentation Index
> Fetch the complete documentation index at: https://quantcontext.ai/docs/llms.txt
> Use this file to discover all available pages before exploring further.

# Quickstart

> Add QuantContext to Claude Desktop or Claude Code in two minutes

## Install the package

```bash theme={null}
pip install quantcontext-mcp
```

## Connect to Claude Desktop

Add this to your `claude_desktop_config.json` (find it at `~/Library/Application Support/Claude/` on macOS):

```json theme={null}
{
  "mcpServers": {
    "quantcontext": {
      "command": "quantcontext"
    }
  }
}
```

Restart Claude Desktop. You'll see `quantcontext` appear in the tools panel.

## Connect to Claude Code

```bash theme={null}
claude mcp add quantcontext -- quantcontext
```

## Verify it's working

Ask Claude:

> "Screen S\&P 500 for value stocks with PE under 15 and ROE above 12%"

Claude will call `screen_stocks` and return a ranked list of candidates with actual market data. The first call downloads and caches market data (\~10s). Subsequent calls use the cache (under 1s for screening, 3–8s for backtesting).

## Try the full workflow

<Steps>
  <Step title="Screen for candidates">
    "Find S\&P 500 stocks trading below 12x earnings with return on equity above 15%"
  </Step>

  <Step title="Backtest the strategy">
    "Backtest a strategy that buys those value stocks, monthly rebalance, over the last 2 years"
  </Step>

  <Step title="Decompose the returns">
    "Run factor analysis on the backtest:where is the return coming from? Is the alpha real?"
  </Step>
</Steps>

## Cache location

Data is cached at `~/.quantcontext/cache/`. Total disk usage is \~50MB for full S\&P 500 coverage.

<Tip>
  The first call to any tool is slow because it downloads market data. After that, screening completes in under a second and backtesting in 3–8 seconds.
</Tip>