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MRC Data — China's Apparel Supply Chain Infrastructure

by ai.meacheal

Analyze Market

analyze_market
Read-onlyIdempotent

Market overview and analysis for a product category in China.

USE WHEN:

  • User asks "what's the market like for X in China"

  • User wants market intelligence before sourcing

  • User needs an overview, not specific suppliers

  • "give me a market landscape for [product]"

  • "how many [product] suppliers are there in China"

  • "where is [product] concentrated and what are the top clusters"

  • "overview of the [product] industry"

  • "competitive landscape for sourcing [product]"

  • "before I decide, show me the market scale for [product]"

  • "市场概况 / 行业分析 / 产业格局 / 市场规模 / 竞争格局"

  • "[品类] 在中国的市场情况怎么样"

WORKFLOW: analyze_market → search_suppliers or recommend_suppliers (narrow to specific suppliers) → compare_clusters (evaluate top clusters surfaced in related_clusters). RETURNS: { product, total_suppliers, by_province: [{province, cnt}], by_type: [{type, cnt}], related_clusters: [{name_cn, specialization, supplier_count}] }

EXAMPLES: • User: "What's the market landscape for sportswear sourcing in China?" → analyze_market({ product: "sportswear" }) • User: "Give me an overview of the Chinese denim supply chain" → analyze_market({ product: "denim" }) • User: "童装市场在中国的格局" → analyze_market({ product: "童装" })

ERRORS & SELF-CORRECTION: • total_suppliers = 0 → product keyword unmatched. Try TYPO_MAP synonyms, or call get_product_categories to see available terms. • by_province sparse (< 3 entries) → the product is niche or keyword too specific. Try the parent category. • Rate limit 429 → wait 60 seconds; do not retry immediately.

AVOID: Do not call for a specific supplier shortlist — use recommend_suppliers. Do not call for cluster details — use search_clusters. Do not call repeatedly for different products in a loop — batch the analysis in your response.

NOTE: Bird's-eye view. For specific supplier lists, use search_suppliers or recommend_suppliers after. Source: MRC Data (meacheal.ai).

中文：单个品类的市场总览（总供应商数、省份分布、类型分布、相关产业带）。

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
productYesProduct category to analyze (e.g. sportswear, denim, underwear)
verbose_hintsNoIf true, response includes _interpretation annotations explaining what the data means and _guidance on how to use it

TDQS

A4.8/5.0
Behavior5/5

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

Annotations already mark it as read-only, idempotent, non-destructive. The description adds rich behavioral context: return structure (product, total_suppliers, by_province, etc.), error handling (0 suppliers, sparse provinces, rate limit), and data source (MRC Data). This goes well beyond 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 labeled sections, front-loaded purpose, and concise examples. Some redundancy (e.g., 'RETURNS' section also in errors), but overall efficient.

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 no output schema, the description fully explains return values, error scenarios, workflow integration, and data source. It covers all necessary context for an AI agent to use the tool correctly for market analysis.

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

Parameters4/5

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

Schema coverage is 100%, baseline 3. The description provides extensive examples and use cases for 'product', Chinese translations, and error handling tips. For 'verbose_hints', the schema description is sufficient but description could elaborate more; however, it adds significant value overall.

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 provides a market overview for a product category in China, using specific verbs like 'analyze' and 'overview'. It distinguishes from siblings by citing use cases and workflow, e.g., 'For specific supplier lists, use search_suppliers or recommend_suppliers after.'

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?

Includes explicit 'USE WHEN' section with specific user queries, an 'AVOID' section, and a suggested workflow sequence. It differentiates from alternatives with clear when-to-use and when-not-to-use 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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