MRC Data

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.

MRC Data is the first MCP (Model Context Protocol) server providingindependently verifiedChinese 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 distinguishesdeclaredvalues (what the supplier claims) fromverifiedvalues (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.

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:

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.

Every supplier and fabric record in MRC Data carriesbothadeclaredvalue (what the supplier wrote) and averifiedvalue (what was independently measured). Each record also carries averified_dimsscore (e.g."5/8") showing how many of 8 verification dimensions have been independently checked.

Example response shape (truncated for readability):

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

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

Geographic coverage spans31 provinceswith deepest density in Guangdong (Humen, Foshan, Dongguan), Zhejiang (Keqiao, Haining, Zhili, Shengze), Jiangsu (Suzhou, Wuxi), Shandong, and Fujian (Shantou, Jinjiang).

- "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."

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:
- Verified ground truthinstead of supplier self-claims, with the gap exposed viaverified_dims
- Industrial-cluster context(the smallest meaningful unit of Chinese manufacturing isn't the factory — it's the 30-minute-drive cluster of subcontracted firms)
- Multi-year relationship graphbetween brands and suppliers, cross-validated against public disclosures
- 24 years of industry-insider contexttranslated 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.

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

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

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

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

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 byDongliang Ye (Dee), CMO of MEACHEAL Group.

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

Official MCP server for dbt (data build tool) providing integration with dbt Core/Cloud CLI, project metadata discovery, model information, and semantic layer querying capabilities.

Provides AI assistants with a secure and structured way to explore and analyze data in GreptimeDB.

Build robust data workflows, integrations, and analytics on a single intuitive platform.

Query and analyze data with MotherDuck and local DuckDB

An MCP server that provides tools to interact with Powerdrill datasets, enabling smart AI data analysis and insights.

Slingshot MCP introduces Slingshot Aerospace’s space data platform in your AI chat—explore satellites, orbits, and Portal insights through Slingshot’s hosted server.

A collection of tools for managing the platform, addressing data quality and reading and writing to Teradata Database.

Interact with Verodat AI Ready Data platform