Category Summary is a paid API for AI agents from api.syntalic.com, paid per call via x402, $0.02/call, status unknown (last checked 2026-09-13).
Returns high-level statistics for a product category including product/brand/retailer counts, pricing aggregates (avg/min/max/median), average rating, promo rate, in-stock rate, and top brands.
High-level category statistics - product/brand/retailer counts, pricing (avg/min/max/median), avg rating, promo rate, in-stock rate, and top brands.
A structured object containing aggregate statistics for the resolved category: total product count, brand count, retailer count, pricing stats (average, min, max, median), average customer rating, promotional rate (fraction of products on promo), in-stock rate, and a list of top brands by presence.
GEThttps://api.syntalic.com/v1/analyst/category-summaryUse this endpoint when you need a broad statistical overview of a product category — pricing benchmarks, brand/product counts, availability, and promo rates — rather than individual product details or time-series trends. It is ideal for market sizing, category health checks, or as a first-pass lookup before drilling into competitive landscape or brand tracker endpoints. Prefer this over Price Dispersion when you need multiple metrics at once rather than deep statistical spread analysis.
{
"country": "us",
"category": "electronics"
}| Field | Type | Description |
|---|---|---|
| country | string | Country (us or ca) |
| category | string | Product category (e.g., electronics, grocery, beauty). Fuzzy human-readable input — the resolver maps it through tier 1-4 (id → exact name → trigram → embedding kNN) to a canonical category_id. Pair with `category_id` if you have a deterministic id from a prior call. |
| department | string | Coarse depth-1 BrowseNodes department label (e.g., 'Electronics', 'Grocery', 'Toys & Games'). Exact-match, case-sensitive Title Case. Optional alongside `category`; either parameter alone or both together are accepted. Use this for stable cross-agent queries where the leaf-level category is too noisy. |
| category_id | integer | Deterministic category id. When set, bypasses the fuzzy resolver cascade and uses tier 1 (id lookup) directly. Agents that learned a canonical id from a prior call's `resolved.category_id` should pin it here for round-trip stability. |
{
"meta": {
"computed_at": "2026-06-16T17:17:25.887Z",
"served_from": "live",
"schema_version": "v3",
"freshness_seconds": 37146
},
"country": "us",
"pricing": {
"avg": 204.26,
"max": 899.99,
"min": 5.99,
"median": 129
},
"category": "electronics",
"currency": "USD",
"excluded": {
"below_floor": 2,
"price_outliers": 111,
"accessory_or_parts": 65,
"refurbished_or_used": 77
},
"resolved": {
"alternates": [],
"category_id": null,
"match_source": "slug",
"category_path": "electronics",
"match_confidence": 1
},
"avg_rating": null,
"department": null,
"top_brands": [
{
"brand": "Sony",
"product_count": 58
},
{
"brand": "Canon",
"product_count": 52
},
{
"brand": "JBL",
"product_count": 37
},
{
"brand": "Amazon",
"product_count": 35
},
{
"brand": "Samsung",
"product_count": 30
},
{
"brand": "Garmin",
"product_count": 24
},
{
"brand": "Soundcore",
"product_count": 18
},
{
"brand": "Panasonic",
"product_count": 17
},
{
"brand": "Kodak",
"product_count": 16
},
{
"brand": "Apple",
"product_count": 16
}
],
"brand_count": 324,
"product_count": 885,
"promo_rate_pct": 13.3,
"retailer_count": 6,
"in_stock_rate_pct": 38.8,
"representative_floor": 5
}{
"type": "object"
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