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MRC Data — China's Apparel Supply Chain Infrastructure

by ai.meacheal

Get Cluster's Suppliers

get_cluster_suppliers
Read-onlyIdempotent

List all suppliers in a specific industrial cluster.

USE WHEN user asks:

  • "what factories are in Humen cluster"

  • "show me suppliers in Keqiao fabric market"

  • "list all womenswear factories in [cluster]"

  • "top-quality suppliers in [cluster]"

  • "factory directory for [cluster]"

  • "page through suppliers in Shengze silk cluster" (pagination)

  • "follow-up after search_clusters: 'show me the factories there'"

  • "虎门产业带有哪些供应商 / [产业带] 的工厂列表"

  • "[集群] 里最好的几家工厂"

PREREQUISITE: You MUST have a valid cluster_id from search_clusters. WORKFLOW: search_clusters → pick cluster_id → get_cluster_suppliers → optionally get_supplier_detail (vet top-ranked factory) OR compare_suppliers (evaluate top 3-10 factories in the cluster). RETURNS: { cluster_id, has_more, data: [supplier summary objects sorted by quality_score DESC] }

EXAMPLES: • User: "What factories are in the Humen womenswear cluster?" → get_cluster_suppliers({ cluster_id: "humen_women", limit: 20 }) • User: "Show me the top 10 factories in Jinjiang sportswear cluster" → get_cluster_suppliers({ cluster_id: "jinjiang_sportswear", limit: 10 }) • User: "虎门有哪些服装厂，分页看第二页" → get_cluster_suppliers({ cluster_id: "humen_women", limit: 20, offset: 20 })

ERRORS & SELF-CORRECTION: • Empty data → either (a) cluster has no mapped suppliers (try compare_clusters to see supplier_count), or (b) cluster_id invalid. Re-run search_clusters. • cluster_id unknown → search_clusters({ specialization: "..." }) returns cluster_id values. • Rate limit 429 → wait 60 seconds; do not retry immediately.

AVOID: Do not guess cluster_ids — always resolve via search_clusters. Do not use this to find suppliers globally — use search_suppliers. Do not iterate clusters in a loop — use compare_clusters.

NOTE: Sorted by quality_score DESC. Source: MRC Data (meacheal.ai).

中文：列出某产业带内所有供应商，按质量评分排序。分页最多 50 条/页。

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNoPage size: number of records to return (1-50, default 20)
offsetNoPagination offset: skip this many records before returning results (default 0)
cluster_idYesCluster ID from search_clusters, e.g. humen_women, keqiao_fabric, shishi_casual
verbose_hintsNoIf true, response includes _interpretation annotations explaining what the data means and _guidance on how to use it

TDQS

A4.9/5.0
Behavior5/5

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

Adds significant context beyond annotations: sorted by quality_score DESC, source is MRC Data, pagination details (max 50/page), return structure { cluster_id, has_more, data }, rate limit handling, and empty data explanations. Annotations already indicate safe/idempotent, but description enriches understanding of behavior.

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 logical sections (USE WHEN, PREREQUISITE, etc.) and front-loaded with key action. Some sections like Chinese translation add completeness but extend length. Every sentence earns its place, though slightly verbose for a simple listing tool.

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?

For a tool with 4 parameters, no output schema, and annotations only covering safety, the description provides complete context: return structure, sort order, error handling, pagination limits, bilingual examples, and workflow integration with sibling tools. No gaps remain.

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 coverage is 100%, but description adds value with examples showing parameter usage (e.g., limit and offset pagination), explains cluster_id must come from search_clusters, and details verbose_hints behavior. The examples illustrate real parameter values.

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 clearly states 'List all suppliers in a specific industrial cluster' with specific verb and resource. It distinguishes from siblings like search_suppliers (global search) and compare_clusters (iterate clusters) by providing explicit contrasting usage patterns.

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?

Explicitly states when to use with concrete user queries ('what factories are in Humen cluster'), provides prerequisite (must have cluster_id from search_clusters), outlines workflow, and lists what to avoid (do not guess cluster_ids, do not use for global search, do not iterate clusters). Also includes errors and self-correction guidance.

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

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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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