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MRC Data — China's Apparel Supply Chain Infrastructure

by ai.meacheal

Get Supplier's Fabric Catalog

get_supplier_fabrics
Read-onlyIdempotent

List all fabrics a specific supplier can provide, with quoted prices.

USE WHEN user asks:

  • "what fabrics does [supplier name] have" / "what can this factory source for me"

  • "show me the catalog of supplier sup_XXX"

  • "what does this manufacturer offer"

  • "what fabric options does sup_XXX quote for denim"

  • "does [supplier] supply [fabric type]"

  • "price list / fabric catalog / offering sheet for sup_XXX"

  • "MOQ per fabric at this supplier"

  • "follow-up: 'what fabrics can they supply?' after identifying a supplier"

  • "[供应商] 能供应哪些面料 / 报价表 / 起订量"

Returns fabric records linked to the supplier with: fabric name, category, weight, composition, and the supplier's quoted price + MOQ for that specific fabric.

PREREQUISITE: You MUST have a valid supplier_id from search_suppliers or get_supplier_detail. WORKFLOW: search_suppliers → get_supplier_detail → get_supplier_fabrics → optionally get_fabric_detail (for lab-test data on a specific fabric) OR get_fabric_suppliers (cross-check price vs other suppliers for same fabric). RETURNS: { supplier_id, count, data: [{ fabric_id, name_cn, category, weight, composition, price_rmb, moq }] }

EXAMPLES: • User: "What fabrics does sup_texhong_042 offer?" → get_supplier_fabrics({ supplier_id: "sup_texhong_042" }) • User: "Show me the fabric catalog and MOQs for sup_001" → get_supplier_fabrics({ supplier_id: "sup_001" }) • User: "sup_234 能做哪些面料，报价多少" → get_supplier_fabrics({ supplier_id: "sup_234" })

ERRORS & SELF-CORRECTION: • count=0 → this supplier has no linked fabric catalog in the database. Either (a) they don't self-source fabrics (CMT-only) — confirm via get_supplier_detail.ownership_type, or (b) their catalog is unmapped — use search_fabrics with their expected specialization instead. • "Supplier not found" (implicit) → the supplier_id is invalid. Re-run search_suppliers. • Rate limit 429 → wait 60 seconds; do not retry immediately.

AVOID: Do not call this for a general fabric search — use search_fabrics. Do not call to compare prices across suppliers for the SAME fabric — use get_fabric_suppliers instead.

NOTE: Source: MRC Data (meacheal.ai). Prices are supplier-quoted, not binding offers.

中文：查询某供应商能供应的所有面料及其报价、起订量。

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
supplier_idYesSupplier ID from search_suppliers, e.g. sup_001
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 indicate read-only and idempotent. Description adds: return fields, error handling for count=0 and invalid IDs, rate limit rules, source note, and that prices are not binding. 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?

Description is long but well-structured with clear sections. Front-loaded with purpose and USE WHEN. Slightly verbose 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?

Despite no output schema, description details return structure. Covers prerequisites, errors, workflow integration with siblings. Highly complete for a complex tool.

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% with 2 parameters. Description adds context: supplier_id format, examples, and explanation of verbose_hints. Baseline 3 with extra value 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 the tool lists all fabrics a supplier can provide with quoted prices. It distinguishes from siblings like search_fabrics and get_fabric_suppliers, providing specific verb and resource.

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 lists when to use (e.g., user asks for supplier's fabrics, catalog, price list) and when not to use (general fabric search, cross-supplier price comparison). Includes prerequisites and workflow context.

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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