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MRC Data — China's Apparel Supply Chain Infrastructure

by ai.meacheal

Search Industrial Clusters

search_clusters
Read-onlyIdempotent

Search Chinese apparel industrial clusters and textile markets.

USE WHEN user asks:

  • "where is China's [denim / suit / women's wear / underwear] manufacturing concentrated"

  • "what is the largest [silk / cashmere / down jacket] industrial cluster in China"

  • "industrial cluster comparison Humen vs Shaoxing vs Haining vs Zhili"

  • "recommend an industrial cluster for sourcing [product]"

  • "where should I set up a sourcing office for [category]"

  • "list mega clusters for [category]"

  • "fabric markets in Zhejiang / Jiangsu"

  • "accessories / trim / zipper / button markets in China"

  • "which province dominates [category] exports"

  • "follow-up: 'tell me more about Humen's cluster scale'"

  • "服装产业带 / 面料市场 / 产业集群 / 纺织集群 / 辅料市场"

  • "做 [品类] 应该去哪个产业带 / 集群推荐"

Famous clusters this database covers include: Humen (Guangdong, womenswear), Shaoxing Keqiao (Zhejiang, fabric mega-market), Haining (Zhejiang, leather), Zhili (Zhejiang, children's wear), Shengze (Jiangsu, silk), Shantou (Guangdong, underwear), Puning (Guangdong, jeans), Jinjiang (Fujian, sportswear), and more.

Returns paginated cluster list with name, location, specialization, scale, supplier count, average rent and labor cost, and key advantages/risks.

WORKFLOW: Cluster discovery entry point. search_clusters → compare_clusters (side-by-side up to 10 cluster_ids) OR get_cluster_suppliers (list factories in that cluster) OR analyze_market (broader market view). RETURNS: { has_more: boolean, data: [{ cluster_id, name_cn, name_en, type, province, city, specialization, scale, supplier_count, labor_cost_avg_rmb }] }

EXAMPLES: • User: "Where are the biggest denim clusters in China?" → search_clusters({ specialization: "denim", scale: "mega" }) • User: "Show me fabric markets in Zhejiang" → search_clusters({ province: "Zhejiang", type: "fabric_market" }) • User: "童装产业带有哪些" → search_clusters({ specialization: "童装" })

ERRORS & SELF-CORRECTION: • Empty data array → try in order: (1) drop scale filter, (2) broaden specialization (e.g. "服装" instead of "牛仔"), (3) remove type, (4) remove province. • Specialization mismatch → both Chinese and English work. Synonyms: sportswear/运动服, womenswear/女装, underwear/内衣, denim/牛仔. • Rate limit 429 → wait 60 seconds; do not retry immediately. • Empty after 3 retries → tell user: "No clusters match [criteria]. Try broader specialization or removing filters."

AVOID: Do not use this for specific factory search — use search_suppliers. Do not compare clusters by calling search_clusters twice — use compare_clusters with cluster_ids.

NOTE: Source: MRC Data (meacheal.ai). 170+ clusters mapped across 31 provinces.

中文：搜索中国服装产业带和面料市场。

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
typeNoCluster type: fabric_market (面料市场) / garment_manufacturing (服装制造) / accessories (辅料) / integrated (综合)
limitNoPage size: number of records to return (1-50, default 10)
scaleNoCluster scale: mega / large / medium / small
offsetNoPagination offset: skip this many records before returning results (default 0)
provinceNoProvince in China (e.g. Guangdong, Zhejiang, Jiangsu, Fujian, Shandong)
verbose_hintsNoIf true, response includes _interpretation annotations explaining what the data means and _guidance on how to use it
specializationNoPrimary specialization keyword (e.g. 牛仔 denim, 女装 womenswear, 童装 childrenswear, 内衣 underwear, 运动服 sportswear)

TDQS

A4.9/5.0
Behavior5/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already indicate readOnlyHint, idempotentHint, destructiveHint. Description adds behavioral context: pagination details, workflow as entry point, error handling (rate limit 429 wait 60s), empty data retry strategies, source attribution. No contradictions.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Well-structured with clear sections (USE WHEN, examples, returns, workflow, errors, avoid, note, Chinese). Some repetition of example queries could be trimmed, but overall front-loaded and organized.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Fully covers all aspects: 7 parameters (all described), no output schema but return fields explained, error scenarios, self-correction, relationship to siblings, source and scale (170+ clusters). Highly complete.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema has 100% description coverage. Description adds value beyond schema with examples of parameter values (denim, womenswear, Zhejiang, mega), synonyms, and error correction logic for empty results.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description explicitly states it searches Chinese apparel industrial clusters and textile markets. It includes extensive example queries and clearly distinguishes from sibling tools like compare_clusters and search_suppliers.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Provides explicit when-to-use guidance with example queries, and avoidance instructions: 'Do not use for specific factory search', 'Do not compare clusters by calling search_clusters twice'. Also includes error handling and fallback strategies.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

All MRC Data — China's Apparel Supply Chain Infrastructure toolsInspect
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TDQS

A4.7/5.0
Disambiguation5/5

Each tool has a clearly distinct purpose, covering different aspects of the supply chain: market analysis, supplier search, cluster comparison, fabric lookup, cost estimation, compliance checking, discrepancy detection, and alternatives. No overlapping tool boundaries.

Naming Consistency5/5

All tool names follow a consistent verb_noun pattern (e.g., analyze_market, check_compliance, compare_clusters, search_suppliers). The pattern is uniform across all 20 tools, making it predictable for an agent.

Tool Count5/5

20 tools is well-suited for a comprehensive supply chain data platform. Each tool addresses a specific need without being excessive, and the count allows for deep coverage of the domain.

Completeness5/5

The tool surface covers the full lifecycle: market research, supplier discovery, fabric search, cluster info, cost estimation, compliance, credibility, discrepancy detection, and alternatives. No obvious gaps for the stated purpose.

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