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CrushRewards Price Dispersion Analyzer
x402
CrushRewards Price Dispersion Analyzer is a paid API for AI agents from api.crushrewards.dev, paid per call via x402, $0.02/call, status unknown (last checked 2026-09-15).
Analyzes price spread within a product category, returning mean, standard deviation, coefficient of variation, and p10–p90 percentiles with IQR-based outlier exclusion.
Price / call$0.02
Success—
StatusUnknown
How this service works
Real-time pricing, promotions, and competitive intelligence across US and Canadian retailers — exposed as paid API endpoints and MCP tools that AI agents can call directly, with x402 and MPP micropayments per query.
Output
Returns a JSON object containing the product category, country, currency, product count, number of outliers excluded via IQR, average price, min/max prices, standard deviation, coefficient of variation, and a full set of percentile breakpoints (p10, p25, p50, p75, p90).
Example use cases
Compare pricing strategies across competitors
“I need to understand how much prices vary within the office supplies category across all Canadian retailers. Can you get me the standard deviation and percentile breakdown — specifically p25 and p75 — so I can see where most competitors are pricing?”
Identify high-volatility product categories
“Which grocery product categories have the most price spread right now? Pull the coefficient of variation for the top 5 grocery subcategories so I can flag which ones are most volatile for our pricing team.”
Detect pricing anomalies and outliers
“Can you analyze the home appliances category in the US and show me how many products got flagged as outliers? I want to see the mean price and IQR details so we can decide if those outliers are legit or data errors.”
Technical details
Endpoint
GEThttps://api.crushrewards.dev/v1/analyst/price-dispersionWhen to prefer
Use this endpoint when you need statistical distribution metrics for prices within a product category — specifically when you need percentiles, standard deviation, or coefficient of variation rather than just a simple average. Prefer this over the category overview endpoint when the goal is to understand price spread and variability, not just central tendency. Ideal for competitive pricing analysis, identifying price clustering, or flagging categories with high price volatility.
Side effects
- Missing required 'category' parameter returns a validation error
- Unknown or unsupported category string may return empty/null statistical fields with zero product count
- Unsupported retailer enum value returns a 400 or validation error
- No products found for the specified category/retailer/country combination results in null stats fields
- Network or upstream data source unavailability may return 5xx errors
Input
{
"country": "us",
"category": "electronics",
"retailer": "amazon"
}Output
{
"meta": {
"computed_at": "2026-06-16T01:26:53.877Z",
"served_from": "live",
"schema_version": "v3",
"freshness_seconds": 69089
},
"country": "us",
"std_dev": 83.36,
"category": "electronics",
"currency": "USD",
"excluded": {
"below_floor": 0,
"price_outliers": 30,
"accessory_or_parts": 5,
"refurbished_or_used": 28
},
"resolved": {
"alternates": [],
"category_id": null,
"match_source": "slug",
"category_path": "electronics",
"match_confidence": 1
},
"avg_price": 87.1,
"max_price": 409,
"min_price": 5.99,
"department": null,
"percentiles": {
"p10": 19.7,
"p25": 28.24,
"p50": 49.99,
"p75": 119.45,
"p90": 199.99
},
"product_count": 179,
"outliers_excluded": 30,
"coeff_of_variation": 0.957,
"representative_floor": 5
}Schema · Response
{
"type": "json"
}Tags
x402
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