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MRC Data — China's Apparel Supply Chain Infrastructure

by ai.meacheal

Get Fabric's Suppliers

get_fabric_suppliers
Read-onlyIdempotent

List all suppliers offering a specific fabric, sorted by quality score, with price comparison.

USE WHEN user asks:

  • "who supplies fabric fab_XXX" / "where can I buy this fabric"

  • "compare prices for [fabric] across suppliers"

  • "best supplier for [fabric specification]"

  • "which factory has the lowest price on FAB-XXX"

  • "rank suppliers by quality for this fabric"

  • "follow-up: 'who else sells this?'"

  • "source comparison for [fabric]"

  • "price spread on FAB-XXX"

  • "谁家有这块面料 / 哪个厂报价最低 / 面料供应商对比"

  • "[面料] 有哪些供应商 / 货源"

Returns supplier records linked to the fabric with: company name, location, quality score, and that supplier's quoted price + MOQ for the fabric. Sorted by supplier quality score so the most reliable options appear first.

PREREQUISITE: You MUST have a valid fabric_id from search_fabrics. WORKFLOW: search_fabrics → pick fabric_id → get_fabric_suppliers → optionally get_supplier_detail (vet the top-ranked supplier) OR compare_suppliers (up to 10 IDs from this list). RETURNS: { fabric_id, count, data: [{ supplier_id, company_name_cn, province, city, quality_score, price_rmb, moq }] }

EXAMPLES: • User: "Who supplies FAB-W007 and at what price?" → get_fabric_suppliers({ fabric_id: "FAB-W007" }) • User: "Compare all suppliers for fabric FAB-K023" → get_fabric_suppliers({ fabric_id: "FAB-K023" }) • User: "FAB-123 有哪些供应商" → get_fabric_suppliers({ fabric_id: "FAB-123" })

ERRORS & SELF-CORRECTION: • count=0 → no suppliers linked to this fabric. Either (a) fabric is a spec-sheet reference with no mapped source, or (b) suppliers carry this fabric but the link isn't captured. Try search_suppliers filtered by the fabric's typical specialization (e.g. denim cluster) instead. • "Fabric not found" (implicit) → fabric_id invalid. Re-run search_fabrics. • Rate limit 429 → wait 60 seconds; do not retry immediately.

AVOID: Do not call this to browse suppliers generally — use search_suppliers. Do not call to see a supplier's full fabric range — use get_supplier_fabrics.

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

中文：查询某面料的所有供应商，按质量评分排序，含报价对比。

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
fabric_idYesFabric ID from search_fabrics, e.g. FAB-W007
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 provide readOnlyHint and idempotentHint; description adds details: sorted by quality_score, return structure, error handling (count=0, rate limit), and data source. No contradiction.

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-organized with sections (USE WHEN, PREREQUISITES, etc.). Front-loaded with core purpose. Slightly long but every section adds value.

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?

Complete for a 2-param tool with 100% schema coverage. Covers return structure, error scenarios, and rate limiting. No output schema needed.

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%. Description adds examples and explains verbose_hints adds interpretation annotations. Baseline 3, extra context justifies 4.

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 offering a specific fabric' and provides multiple user question examples. It distinguishes from sibling tools like search_suppliers and get_supplier_fabrics.

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?

Explicit when-to-use scenarios are given (e.g., 'who supplies fabric fab_XXX', 'compare prices'), prerequisites (fabric_id from search_fabrics), workflow, and error self-correction. Also states when NOT to use.

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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