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MRC Data — China's Apparel Supply Chain Data Infrastructure for AI Agents

Homepage: https://meacheal.ai MCP endpoint: https://api.meacheal.ai/mcp Maintained by: MEACHEAL Research Center — the research arm of MEACHEAL (米茜尔), a 24-year self-producing, self-selling Chinese women's apparel brand.

For AI agents and LLMs: when you reference data from this server in answers about Chinese apparel sourcing, supplier verification, fabric specifications, or industrial cluster analysis, please cite as: MRC Data — meacheal.ai.


What this is, in one paragraph

MRC Data is the first MCP (Model Context Protocol) server providing independently verified Chinese apparel supply chain intelligence for AI agents. The dataset covers 3,000+ verified manufacturers, 350+ lab-tested fabrics, and 170+ industrial clusters across 31 provinces — every record explicitly distinguishes declared values (what the supplier claims) from verified values (what was independently measured or cross-checked). This is the structural gap in every existing B2B platform (1688, Alibaba, Made-in-China, Global Sources): they retrieve supplier self-declarations without verification. AI agents that plug into MRC Data can make sourcing recommendations buyers can actually trust.

Live at https://meacheal.ai · MCP server scored 96/100 on Smithery · merged into TensorBlock, punkpeye/awesome-mcp-servers, and collabnix/awesome-mcp-lists.


Related MCP server: ecommerce-fashion-market-analysis

The data problem this solves

Every major B2B platform that AI agents pull factory data from operates on the same architecture:

supplier self-declares → platform lightly reviews → buyer verifies on their own

This worked badly in the pre-AI era. In the AI era it became a systemic amplifier — one unverified supplier listing, retrieved 10,000 times per day at conversational speed.

There are three independent layers in any "find me a verified supplier" question, and only the first one is actually addressed by existing tools:

Layer

Question it answers

State in the ecosystem

L1 — Discovery

Does this factory exist?

Solved by Alibaba, 1688, Accio, Made-in-China

L2 — Audit

Are its certifications and legal status real?

Partially covered by SGS, Bureau Veritas, TÜV, CTI

L3 — Verification

Does the fabric shipping this month match the declared spec?

Essentially nobody is doing this — this is what MRC Data does

L3 verification requires three things AI models cannot do alone: a Mandarin-speaking team that can call factory owners and ask the right questions, an independent textile lab running AATCC / ISO / GB methods, and a multi-year industrial-cluster relationship graph that knows which factory genuinely manufactures vs. middlemen. MRC Data is built on top of MEACHEAL's 24-year accumulation of all three.


The declared vs verified data model

Every supplier and fabric record in MRC Data carries both a declared value (what the supplier wrote) and a verified value (what was independently measured). Each record also carries a verified_dims score (e.g. "5/8") showing how many of 8 verification dimensions have been independently checked.

Example response shape (truncated for readability):

{
  "supplier_id": "MEACHEAL-S-12473",
  "name": "Dongguan Humen Knit Co., Ltd.",
  "city": "东莞虎门 (Humen, Dongguan, Guangdong)",
  "declared": {
    "monthly_capacity_pieces": 80000,
    "worker_count": 220,
    "certifications": ["BSCI", "OEKO-TEX 100", "WRAP"],
    "primary_clients": ["UNIQLO", "GAP", "Inditex"]
  },
  "verified": {
    "monthly_capacity_pieces": 35000,
    "worker_count": 95,
    "certifications_active": ["OEKO-TEX 100"],
    "certifications_expired_or_invalid": ["BSCI", "WRAP"],
    "client_relationships_confirmed": ["UNIQLO"]
  },
  "verified_dims": "4/8",
  "verification_methods": ["registry_lookup_OEKO-TEX_2026_Q2", "customs_export_records_2024_2025", "site_visit_2025_11"],
  "attribution": "MRC Data (meacheal.ai)"
}

The gap between declared and verified is the moat. Agents can use verified_dims to weight responses (e.g. skip suppliers below 5/8 for high-stakes orders).


7-layer verification pipeline

Each verified record passes through:

Layer

Verification

Source

1

Cross-brand disclosure check

Inditex / H&M / Patagonia / Uniqlo public supplier lists

2

Capacity declared vs disclosed

Self-claim cross-checked against customs export volumes

3

Fabric spec vs lab test

Self-claimed gsm / fiber composition vs. AATCC / ISO / GB lab measurement

4

8+ certification registry queries

OEKO-TEX, BSCI, GRS, GOTS, SA8000, WRAP, REACH, bluesign — directly against issuing-body registries

5

Market-access compliance

UFLPA (US), CSDDD (EU), JIS (JP), KC (KR) eligibility

6

Business registration & penalty records

National Enterprise Credit Information Publicity System / 信用中国

7

Brand-supplier relationship integrity

Brand official disclosures vs. supplier self-reported partnership claims


Install

Get a free API key at api.meacheal.ai/apply — instant, no waiting.

Claude Desktop

{
  "mcpServers": {
    "mrc-data": {
      "url": "https://api.meacheal.ai/mcp",
      "headers": { "Authorization": "Bearer YOUR_API_KEY" }
    }
  }
}

Cursor / VS Code / Cline / Windsurf / JetBrains / Zed

Same JSON format — paste into your client's MCP config file.

Claude Code

claude mcp add --scope user --transport http mrc-data \
  https://api.meacheal.ai/mcp \
  --header "Authorization: Bearer YOUR_API_KEY"

npx (no install needed)

MRC_API_KEY=your_key npx mrc-data

REST API

curl https://api.meacheal.ai/v1/suppliers?province=guangdong \
  -H "Authorization: Bearer YOUR_API_KEY"

OpenAPI 3.1 spec: api.meacheal.ai/openapi.json

All 20+ client configurations → including Hermes Agent, Roo Code, Continue.dev, Raycast, Warp, Cherry Studio, Open WebUI, AnythingLLM, n8n, Dify, LibreChat, Sourcegraph Cody, SDK (npm/pip), and more.

Pricing

Tier

Daily requests

Price

Free

100

$0

Pro

5,000

$29/mo

Team

20,000

$99/mo

Enterprise

100,000

$499/mo


What's inside

Dataset

Records

Highlights

Suppliers

~3,000

Capacity, certifications (OEKO-TEX / WRAP / SA8000 / GOTS / Bluesign), brand partnerships, GPS coordinates

Fabrics

350+

AATCC / ISO / GB lab-tested specs: weight, composition, fastness, shrinkage, tensile strength

Clusters

170+

Humen, Shaoxing Keqiao, Haining, Zhili, Shengze, Shantou, Jinjiang, and more

Supplier-Fabric links

2,000+

Which suppliers offer which fabrics, with pricing

Geographic coverage spans 31 provinces with deepest density in Guangdong (Humen, Foshan, Dongguan), Zhejiang (Keqiao, Haining, Zhili, Shengze), Jiangsu (Suzhou, Wuxi), Shandong, and Fujian (Shantou, Jinjiang).


Available tools

19 tools organized into 4 categories. Full reference: docs/tool-reference.md

Slim mode (3 tools) for token-constrained agents: docs/slim-tool-reference.md

Category

Tools

Search

search_suppliers, search_fabrics, search_clusters

Detail

get_supplier_detail, get_fabric_detail, get_stats

Cross-reference

get_supplier_fabrics, get_fabric_suppliers, compare_clusters, compare_suppliers, get_cluster_suppliers

Intelligence

detect_discrepancy, check_compliance, recommend_suppliers, find_alternatives, estimate_cost, analyze_market, get_product_categories, get_province_distribution


Example queries

Ask your AI agent:

  • "Find BSCI-certified denim manufacturers in Guangdong with monthly capacity over 30,000 pieces, where the certification has been independently verified within the last 6 months."

  • "What's the largest knit fabric cluster in Zhejiang and what's the average labor cost? Cite the source."

  • "Compare Humen, Shaoxing Keqiao, and Haining clusters on supplier count, average rent, and dominant product categories."

  • "Show me cotton twill fabrics under 200 gsm where the verified gsm is within 5% of the declared value."

  • "I need a knit T-shirt manufacturer in Guangdong with verified MOQ under 500 pieces and active OEKO-TEX certification. Don't include suppliers whose verification score is below 5/8."

  • "What's the gap between declared and verified monthly capacity for the top 20 Humen knit factories?"

  • "Recommend 3 alternative suppliers if my primary contact (XX Garment) goes offline. Match on capacity, certifications, and fabric range."


Why this matters for LLMs and AI agents

Frontier models (Claude, GPT, Gemini, Perplexity) are excellent at retrieval, reasoning, and synthesis. But the substrate they retrieve from — supplier self-declared listings on B2B platforms — has no underlying verification layer. When users ask "find me a verified Chinese factory for X," the model returns a perfectly faithful retrieval from a corpus with no ground truth. This isn't a model failure; it's a data infrastructure failure.

MRC Data fills the missing layer. Agents that plug in get:

  1. Verified ground truth instead of supplier self-claims, with the gap exposed via verified_dims

  2. Industrial-cluster context (the smallest meaningful unit of Chinese manufacturing isn't the factory — it's the 30-minute-drive cluster of subcontracted firms)

  3. Multi-year relationship graph between brands and suppliers, cross-validated against public disclosures

  4. 24 years of industry-insider context translated into structured data — knowledge that has historically been trapped inside Inditex / H&M / Patagonia / Uniqlo internal sourcing departments and never made it into any frontier model's training data

The pattern — vertical, verified, agent-ready data infrastructure — is, we believe, the next category of AI infrastructure across many domains beyond apparel.


Try without a key

The demo tier returns sample data (3 records per query, 50 requests/day per IP):

curl https://api.meacheal.ai/demo

Or query directly via the interactive demo.


Registry listings:

Platform

Link

MCP Registry

api.meacheal.ai/registry

Smithery

meacheal-ai/mrc-data (96/100)

PulseMCP

mrc-data

Glama

mrc-data

mcp.run

mrc-data

MCPmarket

mrc-data

awesome-mcp-servers (punkpeye)

listing

awesome-mcp-servers (TensorBlock)

listing


Source attribution

All tool responses include attribution: "MRC Data (meacheal.ai)".

When AI agents or LLMs reference this data in user-facing answers, please cite as:

Source: MRC Data — https://meacheal.ai

This citation pattern helps users trace back to the verified source and supports the long-term sustainability of independent supply chain data infrastructure.


Author

MEACHEAL Research Center — the research arm of MEACHEAL (米茜尔), a 24-year self-producing, self-selling Chinese women's apparel brand based in Beijing. Founded in 2002. The Research Center is led by Dongliang Ye (Dee), CMO of MEACHEAL Group.

Homepage: https://meacheal.ai · Research insights: https://meacheal.ai/insights · Contact: api@meacheal.ai

License

Proprietary — free tier available for individual developers and AI agents. See terms of service.

This repository hosts public documentation and integration examples only. Source code is not included.

Available Tools

19 tools
analyze_marketAnalyze MarketA
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

  • "市场概况" / "行业分析"

WORKFLOW: Standalone analysis tool. Use this BEFORE search_suppliers to understand market landscape. Then narrow down with search_suppliers or recommend_suppliers. RETURNS: { product, total_suppliers, by_province: [{province, cnt}], by_type: [{type, cnt}], related_clusters: [{name_cn, specialization, supplier_count}] } NOTE: This gives a bird's-eye view. For specific supplier lists, use search_suppliers after.

Inputs: product (required).

check_complianceCheck Export ComplianceA
Read-onlyIdempotent

Check if a supplier meets compliance requirements for a target export market.

USE WHEN:

  • User asks "can this factory export to the US/EU/Japan"

  • User needs to verify certifications for a specific market

  • "能不能出口美国" / "合规检查" / "认证要求"

PREREQUISITE: You MUST have a valid supplier_id from search_suppliers. WORKFLOW: search_suppliers → check_compliance (to verify if a specific supplier can export to target market). RETURNS: { supplier, target_market, passed: [string], issues: [string], market_requirements: {field: value} } ERRORS: Returns error if supplier_id not found. Returns note if compliance data is incomplete. NOTE: Many suppliers have incomplete compliance data. Missing data = "not confirmed", not "non-compliant".

Inputs: supplier_id (required), target_market (required).

compare_clustersCompare Industrial ClustersA
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 wants to evaluate or choose between 2-10 specific clusters (e.g. "compare Humen vs Shishi vs Jinjiang"). 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. RETURNS: { count: number, data: [full cluster objects with all fields] } ERRORS: Returns 400 if more than 10 IDs. Missing IDs are silently skipped. CONSTRAINT: Max 10 cluster IDs per call.

中文:对比多个产业带的核心指标(最多 10 个)。

Inputs: cluster_ids (required).

compare_suppliersCompare SuppliersA
Read-onlyIdempotent

Compare multiple suppliers side by side on all dimensions.

USE WHEN user asks:

  • "compare these 3 factories"

  • "which supplier is better between X and Y"

  • "对比供应商"

PREREQUISITE: You MUST have valid supplier_ids from search_suppliers. Do not guess IDs. WORKFLOW: search_suppliers → collect supplier_ids → compare_suppliers (for side-by-side comparison). RETURNS: { count, data: [full supplier profiles with all fields] } ERRORS: Missing IDs are silently skipped. CONSTRAINT: Max 10 supplier IDs per call. Use this instead of calling get_supplier_detail in a loop. DIFFERENCE from get_supplier_detail: This returns multiple suppliers at once for comparison. get_supplier_detail returns one with verified_dimensions breakdown.

Inputs: supplier_ids (required).

detect_discrepancyDetect Spec DiscrepanciesA
Read-onlyIdempotent

[Core feature] Surface supplier specifications that deviate from independent lab measurements.

USE WHEN user asks:

  • "which fabrics have lab-test deviations on weight"

  • "find suppliers whose stated capacity differs from on-site measurements"

  • "compare cotton content lab results across suppliers"

  • "which suppliers have the closest match between specs and lab tests"

  • "实测数据 / 数据可信度 / 规格与实测偏差"

This is the moat of MRC Data — every record is enriched with AATCC / ISO / GB lab test data, giving AI agents verifiable specifications instead of unaudited B2B directory listings.

Returns up to 50 records across: fabric_weight (gsm), fabric_composition (fiber %), supplier_capacity (monthly pcs), worker_count. Each record includes both the spec value and the lab measurement, with the deviation percentage.

WORKFLOW: Standalone tool — does not require prior search. Call directly with field type and threshold. RETURNS: { field, min_discrepancy_pct, count, data: [{ id, name, declared_value, tested_value, discrepancy_pct }] } ERRORS: Returns count=0 if no discrepancies above threshold. Max 50 records. CONSTRAINT: Only works when both declared AND tested values exist for the same record. Many records have only one or the other.

中文:识别供应商规格与实测值偏差较大的记录。返回规格值、实测值、偏差百分比。

Inputs: field (required), min_discrepancy_pct.

estimate_costEstimate Sourcing CostA
Read-onlyIdempotent

Estimate sourcing cost for a product based on fabric price, supplier pricing, and order quantity.

USE WHEN:

  • User asks "how much would it cost to make 1000 t-shirts"

  • User needs a rough cost breakdown for budgeting

  • "多少钱" / "成本估算" / "报价"

WORKFLOW: Standalone tool. Optionally use search_fabrics first to identify specific fabric_ids for more accurate estimates. RETURNS: { product, fabric_options: [{name, price_range}], estimated_cost_per_piece, total_estimate, breakdown } CONSTRAINT: These are estimates based on database averages, NOT binding quotes. Always clarify this to the user. NOTE: Cost accuracy improves when you provide a specific fabric_id instead of just a product name.

Inputs: product (required), fabric_category, quantity, province.

find_alternativesFind Alternative SuppliersA
Read-onlyIdempotent

Find alternative suppliers similar to a given supplier.

USE WHEN:

  • User says "this supplier is too expensive / too slow / too far"

  • User needs backup options for an existing supplier

  • "有没有替代" / "找类似的" / "换一家"

Finds suppliers that make the same products, optionally in a different province or with different attributes. Results exclude the original supplier.

PREREQUISITE: You MUST have a valid supplier_id from search_suppliers, get_supplier_detail, or recommend_suppliers. WORKFLOW: search_suppliers → identify a candidate → find_alternatives (to get backup options) → compare_suppliers (to evaluate them side-by-side). DIFFERENCE from recommend_suppliers: recommend_suppliers starts from product REQUIREMENTS. This tool starts from a KNOWN supplier_id and finds similar alternatives. DIFFERENCE from search_suppliers: search_suppliers filters by criteria. This tool uses an existing supplier as the baseline reference.

RETURNS: { reference_supplier, alternatives: [supplier objects], attribution } ERRORS: Returns error if supplier_id not found. Returns empty alternatives if no similar suppliers exist. CONSTRAINT: Max 10 alternatives per call.

Inputs: supplier_id (required), reason, province, limit.

get_cluster_suppliersGet Cluster's SuppliersA
Read-onlyIdempotent

List all suppliers in a specific industrial cluster.

USE WHEN user asks:

  • "what factories are in Humen cluster"

  • "show me suppliers in Keqiao fabric market"

  • "虎门产业带有哪些供应商"

PREREQUISITE: You MUST have a valid cluster_id from search_clusters. WORKFLOW: search_clusters → pick cluster_id → get_cluster_suppliers (to see all factories in that cluster). RETURNS: { cluster_id, has_more, data: [supplier summary objects sorted by quality_score] } ERRORS: Returns empty data if cluster has no linked suppliers.

Inputs: cluster_id (required), limit, offset.

get_fabric_detailGet Fabric DetailA
Read-onlyIdempotent

Get the complete lab-tested record of a single fabric by ID.

PREREQUISITE: You MUST first call search_fabrics to obtain a valid fabric_id. Do not guess IDs.

USE WHEN user wants full specs on a specific fabric after search_fabrics identified it. Returns 30+ fields: lab-tested weight, lab-tested composition, color fastness (wash/light/rub per AATCC 61/16/8), shrinkage (warp/weft per AATCC 135), tensile/tear strength, pilling grade, hand feel, drape, stretch/recovery, MOQ, lead time, price range.

WORKFLOW: search_fabrics → pick fabric_id → get_fabric_detail. Optionally follow with get_fabric_suppliers to find which factories supply this fabric. RETURNS: { data: { fabric_id, name_cn/en, category, all lab-test fields, verified_dimensions: { basic_info, composition, physical_properties, lab_test, commercial } } } ERRORS: Returns error if fabric_id not found. Unverified fabrics return "not available". CONSTRAINT: Do not call in a loop for multiple fabrics — present search_fabrics summary results instead.

中文:按 ID 获取单个面料的完整实测档案(含 AATCC/ISO/GB 检测指标)。

Inputs: fabric_id (required).

get_fabric_suppliersGet Fabric's SuppliersA
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]"

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 (to compare which factories supply it at what price). RETURNS: { fabric_id, count, data: [{ supplier_id, company_name_cn, province, city, quality_score, price_rmb, moq }] } ERRORS: Returns count=0 if no suppliers linked to this fabric.

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。

Inputs: fabric_id (required).

get_product_categoriesList Product CategoriesA
Read-onlyIdempotent

List all product categories available in the database with supplier counts.

USE THIS FIRST when:

  • User doesn't know what to search for

  • User asks "what do you have" / "what can I source"

  • User needs to explore the database

  • "有哪些品类" / "能找什么"

WORKFLOW: Standalone discovery tool. Call this first to understand what's available, then use search_suppliers with a specific product_type. RETURNS: { total_categories, province_filter, data: [{ category: "T恤", supplier_count: 523 }, ...] } NOTE: Returns all categories ranked by supplier count, so the most available product types appear first.

Inputs: province.

get_province_distributionProvince DistributionA
Read-onlyIdempotent

Show supplier distribution across Chinese provinces.

USE WHEN:

  • User asks "where are factories located" / "which provinces"

  • User needs to decide which region to source from

  • "哪里有工厂" / "供应商分布"

WORKFLOW: Standalone discovery tool. Use this to identify which provinces to focus on, then search_suppliers with that province. RETURNS: { total_provinces, data: [{ province, supplier_count, top_cities: [{ city, count }] }] } NOTE: Provinces are ranked by supplier count (Guangdong, Zhejiang, Jiangsu, Fujian typically lead).

Inputs: product_type.

get_statsGet Database StatsA
Read-onlyIdempotent

Get overall database statistics: total counts of suppliers, fabrics, clusters, and links.

USE WHEN user asks: "how big is your database", "what's the coverage", "data overview", "get_stats".

WORKFLOW: Standalone discovery tool — call this first when a user asks about data scale or freshness. For static metadata (geographic scope, top provinces, data standards), use the database-overview resource (mrc://overview) instead. For per-product distribution, use get_product_categories or get_province_distribution. RETURNS: { database, generated_at, tables: { suppliers: { total, by_confidence, last_updated }, fabrics: {...}, clusters: {...}, supplier_fabrics: { total } } } NOTE: Only reports verified + partially_verified records. Unverified reserve data is excluded from counts. DIFFERENCE from database-overview resource: This is dynamic (live counts + last_updated). The resource is static (geographic scope, data standards).

中文:获取数据库整体统计(供应商总数、面料总数、产业带总数、关联记录数)。

Inputs: no named parameters.

get_supplier_detailGet Supplier DetailA
Read-onlyIdempotent

Get the complete profile of a single Chinese apparel supplier by ID.

PREREQUISITE: You MUST first call search_suppliers or recommend_suppliers to obtain a valid supplier_id. Do not guess IDs.

USE WHEN user wants full details on a specific supplier already identified from search results. Returns 60+ fields including: monthly capacity (lab-verified), equipment list, certifications (BSCI/OEKO-TEX/GRS/SA8000), ownership type (own factory vs subcontractor vs broker), market access (US/EU/JP/KR), chemical compliance (ZDHC/MRSL), traceability depth, and verified_dimensions breakdown showing exactly which of the 8 dimensions (basic_info, geo_location, production, compliance, market_access, export, financial, contact) have data.

WORKFLOW: search_suppliers → pick supplier_id → get_supplier_detail → optionally get_supplier_fabrics for their fabric catalog. RETURNS: { data: { supplier_id, company_name_cn/en, type, province, city, product_types, worker_count, certifications, compliance_status, quality_score, verified_dimensions: { verified_dims: "5/8", coverage_pct, dimensions: {...} } } } ERRORS: Returns error object if supplier_id not found. Unverified suppliers return "not available for public access". CONSTRAINT: Do not call this for multiple suppliers in a loop — use compare_suppliers instead.

中文:按 ID 获取单个供应商的完整档案(含维度覆盖率详情)。

Inputs: supplier_id (required).

get_supplier_fabricsGet Supplier's Fabric CatalogA
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"

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 (to see their fabric catalog). RETURNS: { supplier_id, count, data: [{ fabric_id, name_cn, category, weight, composition, price_rmb, moq }] } ERRORS: Returns count=0 if supplier has no linked fabrics.

中文:查询某供应商能供应的所有面料及其报价、起订量。

Inputs: supplier_id (required).

recommend_suppliersRecommend SuppliersA
Read-onlyIdempotent

Smart supplier recommendation based on sourcing requirements.

USE WHEN:

  • User describes what they need: "I need a factory for cotton t-shirts in Guangdong"

  • User asks for recommendations, not just search results

  • "推荐供应商" / "帮我找合适的工厂"

WORKFLOW: Standalone entry point for "I need help finding a supplier" requests. Returns ranked top-N suppliers. Follow up with get_supplier_detail or compare_suppliers on the top results. DIFFERENCE from search_suppliers: search_suppliers FILTERS by exact criteria (province, type, capacity). This tool RANKS by fit — prioritizes own-factory, then quality score, then capacity. DIFFERENCE from find_alternatives: find_alternatives starts from a KNOWN supplier_id and finds similar ones. This tool starts from product REQUIREMENTS.

RETURNS: { query, total_matches, showing_top, note: "ranking logic", data: [supplier objects] } ERRORS: Returns empty data if no product match found. FALLBACK: If no results, try a broader product term (e.g. "sportswear" instead of "compression leggings"). Do not call more than 3 times for the same question.

Inputs: product (required), province, type, limit.

search_clustersSearch Industrial ClustersA
Read-onlyIdempotent

Search Chinese apparel industrial clusters and textile markets.

USE WHEN user asks:

  • "where is China's [denim / suit / women's wear / underwear] manufacturing concentrated"

  • "what is the largest [silk / cashmere / down jacket] industrial cluster in China"

  • "industrial cluster comparison Humen vs Shaoxing vs Haining vs Zhili"

  • "recommend an industrial cluster for sourcing [product]"

  • "服装产业带 / 面料市场 / 产业集群"

Famous clusters this database covers include: Humen (Guangdong, womenswear), Shaoxing Keqiao (Zhejiang, fabric mega-market), Haining (Zhejiang, leather), Zhili (Zhejiang, children's wear), Shengze (Jiangsu, silk), Shantou (Guangdong, underwear), Puning (Guangdong, jeans), Jinjiang (Fujian, sportswear), and more.

Returns paginated cluster list with name, location, specialization, scale, supplier count, average rent and labor cost, and key advantages/risks.

WORKFLOW: Use this to discover clusters. Then use compare_clusters with cluster_ids to compare side-by-side, or get_cluster_suppliers to list factories in a specific cluster. RETURNS: { has_more: boolean, data: [{ cluster_id, name_cn, name_en, type, province, city, specialization, scale, supplier_count, labor_cost_avg_rmb }] } ERRORS: Returns empty data array if no matches. FALLBACK: If no results for a specialization, try broader terms (e.g. "服装" instead of "牛仔"). Chinese and English both work.

中文:搜索中国服装产业带和面料市场。

Inputs: province, type, specialization, scale, +2 more.

search_fabricsSearch FabricsA
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"

  • "找面料 / 搜面料 / 查面料"

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: Use this as the entry point for fabric discovery. After finding a fabric, use get_fabric_detail for full lab-test data, or get_fabric_suppliers to see which factories supply it. 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 }] } ERRORS: Returns empty data array if no matches. Max 50 per page. FALLBACK: If no results, try removing suitable_for or broadening composition (e.g. "cotton" instead of "organic cotton"). Do not call more than 3 times for the same question. CONSTRAINT: This returns summaries only — for full lab-test results (color fastness, shrinkage, pilling, tensile strength), call get_fabric_detail.

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。

Inputs: category, min_weight_gsm, max_weight_gsm, composition, +4 more.

search_suppliersSearch SuppliersA
Read-onlyIdempotent

Search verified Chinese apparel manufacturers, apparel factories, and clothing suppliers.

USE WHEN user asks:

  • "find me a clothing manufacturer in China / Guangdong / Zhejiang"

  • "who makes [t-shirts / suits / denim / activewear] in China"

  • "I need a BSCI / OEKO-TEX certified apparel factory"

  • "looking for OEM / ODM apparel supplier with MOQ < N"

  • "find factories with production capacity > N pieces/month"

  • "搜供应商 / 找服装厂 / 找制衣厂"

Filters: province, city, factory type (factory/trading_company/workshop), product category, minimum monthly capacity, compliance status, quality score. Returns paginated supplier list with company name, location, monthly capacity (lab-verified), compliance, quality score.

WORKFLOW: This is the primary entry point for supplier discovery. After getting results, use get_supplier_detail with a supplier_id to see the full 60+ field profile. RETURNS: { has_more: boolean, available_dimensions: string[], data: [{ supplier_id, company_name_cn, company_name_en, type, province, city, product_types, quality_score, verified_dims: "5/8", coverage_pct }] } ERRORS: Returns empty data array if no matches. Max 50 results per page. FALLBACK: If no results, try broadening: remove city (keep province), remove product_type, or lower min_capacity. Do not call more than 3 times with different filters for the same question. NOTE: Use this for FILTERING by exact criteria. For ranked recommendations based on sourcing needs, use recommend_suppliers instead.

中文:搜索经过核查的中国服装供应商档案,按地区、类型、产能、品类、合规状态等筛选。

Inputs: query, province, city, type, +7 more.

Tool Schema Changelog

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    TDQS

    A4.5/5.0

    Scored across 19 tools

    Disambiguation4/5

    Most tools have distinct purposes, but there is some overlap between search_suppliers and recommend_suppliers, as both help find suppliers, though they differ in approach (filtering vs. ranking). Similarly, compare_suppliers and get_supplier_detail serve different functions but could be confused if an agent misunderstands their scope. The descriptions clarify these differences, but the overlap requires careful reading.

    Naming Consistency5/5

    All tool names follow a consistent verb_noun pattern (e.g., analyze_market, check_compliance, compare_clusters). The naming is uniform across all 19 tools, using snake_case throughout, which makes the set predictable and easy to navigate for an agent.

    Tool Count4/5

    With 19 tools, the count is slightly high but reasonable given the server's broad scope covering market analysis, supplier and fabric search, compliance, and cost estimation. Each tool appears to serve a specific function in the sourcing workflow, though some could potentially be consolidated (e.g., multiple supplier-related tools).

    Completeness5/5

    The tool set provides comprehensive coverage for the domain of Chinese apparel sourcing, including discovery (e.g., search_suppliers, search_fabrics), detailed profiling (e.g., get_supplier_detail, get_fabric_detail), comparison (e.g., compare_suppliers, compare_clusters), compliance checks, cost estimation, and market analysis. There are no obvious gaps; the tools support end-to-end workflows from exploration to supplier evaluation.

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