Province Distribution
get_province_distributionShow supplier distribution across Chinese provinces.
USE WHEN:
User asks "where are factories located" / "which provinces"
User needs to decide which region to source from
"where's [product] manufacturing concentrated in China"
"top provinces for [category]"
"geographic heatmap of suppliers for [product]"
"is sportswear mostly in Fujian or Zhejiang"
"which cities lead denim production"
"follow-up: 'break it down by province'"
"哪里有工厂 / 供应商分布 / 产业分布 / 地域分布"
"[品类] 主要在哪几个省 / 哪个省最集中"
WORKFLOW: Standalone discovery tool. get_province_distribution → search_suppliers (with top province) OR search_clusters (for clusters within that province) OR analyze_market (deeper view). RETURNS: { total_provinces, data: [{ province, supplier_count, top_cities: [{ city, count }] }] }
EXAMPLES: • User: "Where are most Chinese apparel factories located?" → get_province_distribution({}) • User: "Which provinces lead in sportswear manufacturing?" → get_province_distribution({ product_type: "sportswear" }) • User: "牛仔工厂主要分布在哪" → get_province_distribution({ product_type: "denim" })
ERRORS & SELF-CORRECTION: • Empty data for product_type → product_type keyword may not match. Try TYPO_MAP synonyms (tee→t-shirt, jeans→denim, 运动服→activewear) or drop the filter entirely. • Sparse results (< 3 provinces) → the product is niche. Try the parent category or broaden the term. • Rate limit 429 → wait 60 seconds; do not retry immediately.
AVOID: Do not call for cluster-level granularity — use search_clusters. Do not call without product_type if user is asking about a specific category — the unfiltered output is generic.
NOTE: Provinces are ranked by supplier count (Guangdong, Zhejiang, Jiangsu, Fujian typically lead). Source: MRC Data (meacheal.ai).
中文：按省份展示供应商分布，含每省 Top 城市。可按品类筛选。
Input Schema
| Name | Required | Description | Default |
|---|---|---|---|
| product_type | No | Filter by product type (e.g. sportswear, t-shirt, 运动服) | |
| verbose_hints | No | If true, response includes _interpretation annotations explaining what the data means and _guidance on how to use it |