MRC Data — China's Apparel Supply Chain Infrastructure

Compare Industrial Clusters

compare_clusters

Read-onlyIdempotent

Compare multiple Chinese apparel industrial clusters side-by-side on key metrics.

PREREQUISITE: You MUST first call search_clusters to obtain valid cluster_ids. Do not guess IDs.

USE WHEN user asks:

"compare Humen vs Shishi vs Jinjiang"
"which cluster has lower labor cost — Humen or Dongguan"
"side-by-side: Haining vs Xintang for denim"
"evaluate 3 clusters for my sportswear line"
"对比 [产业带1] 和 [产业带2]" / "哪个集群更适合 [品类]"
"rank these clusters by supplier count"
"which cluster has the highest scale for womenswear"
"follow-up: 'now compare the top 3 clusters you just listed'"

Returns full records for each cluster so they can be compared on labor cost, rent, supplier count, scale, specializations, advantages, and risks.

WORKFLOW: search_clusters → collect cluster_ids → compare_clusters → optionally get_cluster_suppliers on the winner to list factories in that specific cluster. RETURNS: { count: number, data: [full cluster objects with all fields] }

EXAMPLES: • User: "Compare Humen, Shishi, and Jinjiang for sportswear sourcing" → compare_clusters({ cluster_ids: ["humen_women", "shishi_casual", "jinjiang_sportswear"] }) • User: "I want to evaluate Keqiao vs Zhili fabric markets" → compare_clusters({ cluster_ids: ["keqiao_fabric", "zhili_children"] }) • User: "对比虎门、石狮、晋江三个产业带" → compare_clusters({ cluster_ids: ["humen_women", "shishi_casual", "jinjiang_sportswear"] })

ERRORS & SELF-CORRECTION: • "Too many IDs (>10)" → split into batches of 10 and aggregate results in your response. • Fewer results than IDs sent → missing IDs were silently skipped (invalid cluster_id). Re-run search_clusters to verify IDs. • Empty data → all IDs were invalid. Re-run search_clusters and try again with fresh IDs. • Rate limit 429 → wait 60 seconds; do not retry immediately.

AVOID: Do not call with guessed cluster_ids — always resolve them via search_clusters first. Do not use to list factories in a cluster — use get_cluster_suppliers. Do not compare > 10 clusters in one call.

CONSTRAINT: Max 10 cluster IDs per call.

NOTE: Source: MRC Data (meacheal.ai).

中文：对比多个产业带的核心指标（最多 10 个）。

Input Schema

TableJSON Schema

Name	Required	Description	Default
`cluster_ids`	Yes	Array of cluster IDs to compare, max 10
`verbose_hints`	No	If true, response includes _interpretation annotations explaining what the data means and _guidance on how to use it

TDQS

A4.6/5.0

Behavior5/5

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

Annotations already declare readOnlyHint, idempotentHint, and destructiveHint, so the safety profile is clear. The description adds behavioral context: returns full records, silently skips invalid IDs, rate limiting, and a max of 10 IDs. No contradiction with annotations.

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?

The description is well-structured with clear sections (purpose, prerequisite, use cases, workflow, examples, errors). It is front-loaded with the purpose. While lengthy, every sentence adds value for a complex tool, making it appropriate.

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?

Given the tool's complexity (2 params, no output schema), the description comprehensively covers use cases, prerequisites, error handling, and workflow linkage to sibling tools. It even includes Chinese and a data source note.

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

Parameters3/5

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

Input schema covers 100% of parameters, so baseline is 3. The description adds workflow context (prerequisite for cluster_ids, batch behavior) but does not add new meaning beyond the schema for the individual parameters.

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 the tool's purpose: 'Compare multiple Chinese apparel industrial clusters side-by-side on key metrics.' It uses specific verbs ('compare') and resources ('industrial clusters'), and distinguishes from siblings like search_clusters and get_cluster_suppliers through explicit workflow and avoidance instructions.

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?

The description provides explicit when-to-use scenarios with example user queries, a prerequisite (must call search_clusters first), and what to avoid (do not guess IDs, do not use for listing factories). It also includes error handling for too many IDs, invalid IDs, and rate limits.

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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