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MRC Data — China's Apparel Supply Chain Infrastructure

by ai.meacheal

Search Fabrics

search_fabrics
Read-onlyIdempotent

Search the Chinese fabric and textile database with lab-tested specifications.

USE WHEN user asks:

  • "find me a [cotton / polyester / nylon / wool / linen] fabric for [t-shirts / jeans / suits]"

  • "I need 180gsm jersey knit with verified composition"

  • "fabrics under N RMB/meter for womenswear"

  • "compare lab-tested fabric weight across suppliers"

  • "show me functional fabrics for activewear / sportswear"

  • "what woven fabrics work for shirting"

  • "list organic / GOTS / recycled fabrics"

  • "I want heavyweight denim above 12 oz"

  • "fabrics with stretch / spandex content 2-5%"

  • "give me another page" (pagination via offset)

  • "lab-verified composition for [product]" (quality check)

  • "找面料 / 搜面料 / 查面料 / 找布料 / 打样面料"

  • "我要做 T 恤，帮我找克重 180-220 的针织面料"

Filters: category (woven/knit/nonwoven/leather/functional), weight range (gsm), composition keyword, target apparel type, max price. Returns paginated fabric list with name, lab-tested weight, lab-tested composition, price range, suitable apparel, and data confidence level.

WORKFLOW: Primary entry point for fabric discovery. search_fabrics → get_fabric_detail (full 30+ lab-test fields) OR get_fabric_suppliers (compare supplier prices for same fabric) OR estimate_cost (budget the product). RETURNS: { has_more: boolean, available_dimensions: ["basic_info","composition","physical_properties","lab_test","commercial"], data: [{ fabric_id, name_cn, category, subcategory, declared_weight_gsm, declared_composition, price_range_rmb, suitable_for, verified_dims: "4/5", coverage_pct }] }

EXAMPLES: • User: "Find 180-220gsm cotton jersey for t-shirts under 35 RMB/m" → search_fabrics({ category: "knit", min_weight_gsm: 180, max_weight_gsm: 220, composition: "cotton", suitable_for: "t-shirt", max_price_rmb: 35 }) • User: "I need stretch denim for women's jeans" → search_fabrics({ category: "woven", composition: "spandex", suitable_for: "denim" }) • User: "帮我找适合做衬衫的梭织面料，棉 60% 以上" → search_fabrics({ category: "woven", composition: "cotton", suitable_for: "shirt" })

ERRORS & SELF-CORRECTION: • Empty data array → try in order: (1) drop suitable_for, (2) widen weight range by 50gsm each side, (3) broaden composition (e.g. "cotton" instead of "organic cotton"), (4) drop max_price_rmb, (5) try the parent category (knit → all). • Composition mismatch → TYPO_MAP normalizes common misspellings (e.g. "poly" → "polyester", "lycra" → "spandex"). If still no match, try the Chinese term (棉/涤纶/氨纶/锦纶). • Rate limit 429 → wait 60 seconds. Do not retry immediately. • Empty after 3 retries → tell user: "No fabric matches [criteria]. Would you like to broaden weight/price/composition?"

AVOID: Do not call this looking for a specific named fabric SKU — search by specs instead (weight + composition + category). Do not fetch full lab-test data this way — use get_fabric_detail. Do not call repeatedly for supplier pricing on the same fabric — use get_fabric_suppliers.

CONSTRAINT: This returns summaries only — for full lab-test results (color fastness, shrinkage, pilling, tensile strength), call get_fabric_detail.

NOTE: Source: MRC Data (meacheal.ai). Every record includes AATCC / ISO / GB lab test measurements where verified.

中文：搜索面料数据库，按品类、克重、成分、适用品类、价格筛选。每条均含 AATCC / ISO / GB 方法的实测数据。

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNoPage size: number of records to return (1-50, default 10)
offsetNoPagination offset: skip this many records before returning results (default 0)
categoryNoFabric category: woven (梭织) / knit (针织) / nonwoven (无纺) / leather (皮革) / fur (毛皮) / functional (功能性)
compositionNoFiber composition keyword (e.g. cotton, polyester, spandex, nylon, wool, linen, 棉, 涤纶)
suitable_forNoTarget apparel keyword (e.g. T恤 t-shirt, 衬衫 shirt, 牛仔 denim, 连衣裙 dress)
max_price_rmbNoMaximum price in RMB per meter
verbose_hintsNoIf true, response includes _interpretation annotations explaining what the data means and _guidance on how to use it
max_weight_gsmNoMaximum fabric weight in grams per square meter
min_weight_gsmNoMinimum fabric weight in grams per square meter

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. The description goes far beyond: it discloses pagination behavior, error self-correction steps, composition typo normalization, rate limit handling, Chinese language support, data source (MRC Data), and that it returns summaries only (not full lab tests). No contradictions 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 long but well-structured with headings (USE WHEN, WORKFLOW, RETURNS, EXAMPLES, ERRORS, AVOID, CONSTRAINT, NOTE). It is front-loaded with purpose and use cases. Every section adds value, though some repetition exists (e.g., the Chinese translation at the end). Overall, it's efficient given the tool's complexity.

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?

The description is fully self-contained. It explains the return format with field names, pagination (has_more), available dimensions, and error handling. It covers edge cases (empty results, composition mismatches, rate limiting) and provides example API calls. Without an output schema, the agent has all necessary context to use the tool effectively.

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?

Schema coverage is 100% with each parameter having a description. The description does not add further meaning to individual parameters beyond the schema, but it does provide examples showing how parameters are combined in real queries. Following the guideline that high schema coverage defaults to 3, and given that the examples enhance understanding but don't extend parameter semantics, a score of 3 is appropriate.

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?

Description opens with a clear verb+resource: 'Search the Chinese fabric and textile database with lab-tested specifications.' The USE WHEN section lists many concrete user queries, and the AVOID section explicitly distinguishes it from siblings like get_fabric_detail and get_fabric_suppliers, making the purpose unmistakable.

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?

Extensive guidance on when to use this tool: a dedicated USE WHEN list with varied query examples, a WORKFLOW section showing how it fits into an agent flow, and an AVOID section that explicitly says when NOT to use it (e.g., for specific SKU lookup, full lab-test data, or supplier pricing). This fully equips the agent to choose correctly.

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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