Computes the arithmetic mean of numerical datasets with precision handling for statistical analysis. Use via MCP in AI coding tools or REST API at GET /v1/stats/mean. Pass arrays like [1,2,3,4,5] to get 3.0 as output. Handles floating-point precision correctly unlike basic division operations that can introduce rounding errors.
curl "https://tinyfn.io/v1/stats/mean" \
-H "X-API-Key: YOUR_API_KEY"const response = await fetch('https://tinyfn.io/v1/stats/mean', {
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/mean',
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"
}
}
}
}Call the mean function with your numerical array. The MCP tool handles the computation and returns the precise arithmetic average without floating-point precision issues.
This tool handles edge cases like empty arrays, maintains numerical precision, and provides consistent output formatting. Basic division can introduce floating-point errors.
Works with datasets of any practical size. The tool efficiently processes large numerical arrays without memory constraints typical in client-side calculations.
Yes, processes any numerical values including negatives, decimals, and scientific notation. Returns the mathematically correct arithmetic mean for mixed number types.
Returns appropriate error handling for edge cases like empty datasets. The tool validates input and provides meaningful error messages rather than undefined behavior.
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