Statistics Utilities

Describe Data

Calculate comprehensive descriptive statistics for numerical datasets including mean, median, standard deviation, quartiles, and distribution properties. Access via MCP in Cursor or Windsurf, or REST at `/v1/stats/describe`. Pass arrays of numbers to get instant statistical summaries — no manual calculations or library imports needed for AI agents analyzing data.

API Endpoint

GET /v1/stats/describe

Code Examples

curl "https://tinyfn.io/v1/stats/describe" \
  -H "X-API-Key: YOUR_API_KEY"
const response = await fetch('https://tinyfn.io/v1/stats/describe', {
  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/describe',
    headers={'X-API-Key': 'YOUR_API_KEY'})
data = response.json()
print(data)

Use via MCP

Add to your AI agent

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"
      }
    }
  }
}

Learn more about MCP setup →

Related Tools

Calculate Mean
Calculate arithmetic mean (average).
Calculate Median
Calculate median.
Calculate Mode
Calculate mode (most frequent value).
Calculate Variance
Calculate variance.
Calculate Stddev
Calculate standard deviation.
Calculate Percentile
Calculate a specific percentile.
Calculate Quartiles
Calculate quartiles (Q1, Q2, Q3).
Calculate Range
Calculate range (max - min).

FAQ

What descriptive statistics does this tool calculate?

Returns count, mean, median, mode, standard deviation, variance, min/max values, quartiles (Q1, Q3), interquartile range, skewness, and kurtosis for comprehensive dataset analysis.

How do I use describe data in MCP tools like Cursor?

Call the describe_data function with your numerical array. The MCP tool processes the data locally and returns formatted statistical output without external API calls.

Can I analyze datasets with missing values or non-numeric data?

The tool filters out null, undefined, and non-numeric values automatically, then calculates statistics on valid numbers only. Empty arrays return appropriate null statistics.

What's the difference between this and pandas describe()?

Similar output format but designed for AI agents — no Python environment needed. Returns JSON-structured results optimized for programmatic use and cross-language compatibility.

How large datasets can I analyze with this statistics tool?

Handles arrays up to 10,000 elements efficiently. For larger datasets, consider sampling or using dedicated analytics platforms for memory-intensive statistical computations.

Try Describe Data Now

Get your free API key and start using Describe Data in seconds.

Get Free API Key