meacheal-ai/mrc-data

**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**.
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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.
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The data problem this solves
Every major B2B platform that AI agents pull factory data from operates on the same architecture:
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.
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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):
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).
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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 |