Calculates z-score (standard score) to measure how many standard deviations a value is from the mean. Access via MCP in Cursor or Windsurf, or REST API at `/v1/stats/z-score`. Input a value, mean, and standard deviation to get the normalized score. Essential for statistical analysis and outlier detection in AI data processing workflows.
curl "https://tinyfn.io/v1/stats/z-score" \
-H "X-API-Key: YOUR_API_KEY"const response = await fetch('https://tinyfn.io/v1/stats/z-score', {
headers: { 'X-API-Key': 'YOUR_API_KEY' }
});
const data = await response.json();
console.log(data);import requests
response = requests.get('https://tinyfn.io/v1/stats/z-score',
headers={'X-API-Key': 'YOUR_API_KEY'})
data = response.json()
print(data)Connect your AI agent (Claude, Cursor, Windsurf, etc.) to TinyFn's statistics tools:
{
"mcpServers": {
"tinyfn-stats": {
"url": "https://tinyfn.io/mcp/stats",
"headers": {
"X-API-Key": "YOUR_API_KEY"
}
}
}
}Pass the value, mean, and standard deviation to the z-score tool. It returns (value - mean) / standard_deviation as the standardized score.
The value is 2.5 standard deviations below the mean. Values beyond ±2 are typically considered outliers in normal distributions.
Yes, MCP agents in Cursor or Claude Code can call this tool to standardize datasets, identify outliers, or compare values across different scales.
The calculation returns undefined or infinity since you cannot divide by zero. All values in the dataset would be identical.
Z-score measures distance from mean in standard deviations, while percentiles show relative ranking. Z-score assumes normal distribution, percentiles work with any distribution.
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