MRC Data API

AI-Native Apparel Supply Chain Data

MRC Data API is purpose-built for LLMs, AI agents, and RAG systems. Verified suppliers, 350+ lab-tested fabrics, 170+ industrial clusters across China. 19 MCP tools, REST API, and OpenAPI 3.1 spec.

1. The Data Problem AI Applications Face

When you build AI-powered apparel sourcing applications, have you encountered these problems?

2. Traditional vs AI-Native Supply Chain Data

3. What Is "AI-Native" Supply Chain Data?

Structured & Standardized

All data outputs in unified JSON format with consistent field naming. AI models can parse directly — no complex preprocessing.

{
  "data": {
    "supplier_id": "sup_001",
    "company_name_cn": "广州新鑫服饰有限公司",
    "province": "广东",
    "type": "factory",
    "quality_score": 8.5,
    "compliance_status": "compliant",
    "certifications": ["BSCI", "OEKO-TEX", "GRS"],
    "monthly_capacity": 50000
  },
  "attribution": "MRC Data (meacheal.ai)"
}

API-Native & Tool-Ready

Traditional data requires logging into platforms and browsing manually. MRC Data can be called directly via REST API or MCP tools.

• AI Agent can query data "like a human"
• No complex data scraping logic needed
• Supports Claude, Cursor, ChatGPT, LangChain, Dify, and more

Lab-Verified & Trustworthy

Not just raw data — enriched with independent lab measurements and derived metrics:

• Lab-tested specs: fabric weight, shrinkage, color fastness, tensile strength (AATCC/ISO/GB)
• Quality scoring: multi-dimensional composite quality score per supplier
• Discrepancy detection: flag specs that deviate from lab results

AI answers grounded in facts — verifiable and explainable.

4. Why AI Apps Need Dedicated Supply Chain Data

Eliminate "supply chain hallucination"

LLM training data has a cutoff date and may contain errors. When users ask about real suppliers, the model can only fabricate.

Solution: Connect MRC Data via MCP tools — AI gets real, verified data.

Professional scenarios need professional data

Generic B2B platforms and search engines return inaccurate, outdated supply chain information. Professional scenarios have higher standards:

• Accuracy: fabric specs must match independent lab measurements
• Completeness: 50+ fields per supplier, 30+ per fabric
• Cross-referencing: supplier ↔ fabric ↔ cluster ↔ compliance linked
• Compliance: data sourced from legitimate, verifiable channels

AI needs "tools", not "documents"

Traditional supply chain data exists as web pages, PDFs, and spreadsheets — AI can't use them directly. MCP protocol lets LLMs call data like calling functions:

AI thinks: User wants to check if this supplier meets EU compliance.
I need to call the compliance check tool.

→ Call: check_compliance(supplier_id="sup_001", target_market="eu")
→ Returns: {
    "compliant": false,
    "missing": ["REACH", "OEKO-TEX Class I"],
    "has": ["BSCI", "ISO 9001"]
  }

→ AI answers: This supplier currently holds BSCI and ISO 9001,
   but is missing REACH and OEKO-TEX Class I for EU market entry...

5. MRC Data's Differentiation

6. Typical Use Cases

7. Get Started

Ready to connect professional supply chain data to your AI application?

Available protocols

• MCP (Model Context Protocol) — 19 tools for Claude, Cursor, and any MCP client
• REST API — Standard HTTP endpoints for ChatGPT Actions, Gemini, Copilot, custom apps
• OpenAPI 3.1 — Machine-readable spec for SDK generation and code completion

Base URL

https://api.meacheal.ai

MRC Data

Introduction

MRC Data is the apparel supply chain data infrastructure built by MEACHEAL Research Center. Verified suppliers, 350+ lab-tested fabrics, 170+ industrial clusters. 19 MCP tools + REST API + OpenAPI 3.1. Free tier available, Pro from Contact usnth.

What is MRC Data API?

MRC Data API is purpose-built for large language models and AI systems. All data outputs in unified JSON format, accessible via REST API or MCP protocol. Minimal code required — most integrations take under 5 minutes.

• For AI agents: 19 MCP tools let Claude, Cursor, and any MCP client call supply chain data like native functions
• For developers: REST API with standard HTTP endpoints, OpenAPI 3.1 spec, TypeScript & Python SDKs
• For ChatGPT: Import OpenAPI spec as a ChatGPT Action in 2 clicks

How to connect?

Pricing

Who uses MRC Data?

• AI sourcing startups building autonomous procurement agents
• Fashion brands integrating supply chain intelligence into design workflows
• Trading companies automating supplier discovery and compliance checks
• Developers building RAG applications grounded in real supply chain data

Quick links

MRC Data

Data Coverage

MRC Data covers verified suppliers, 350+ fabrics, and 170+ industrial clusters across China's apparel supply chain. All data independently verified by MEACHEAL Research Center with AATCC/ISO/GB lab test methods.

Database at a Glance

Supplier Data (50+ fields)

Fabric Data (30+ fields)

Cluster Data (20+ fields)

Geographic Coverage

Suppliers span 20+ provinces across China, concentrated in the major apparel manufacturing regions:

19 API Tools

Authentication

All /v1/* endpoints require a valid API key in the Authorization header using the Bearer scheme. The /demo and /status endpoints do not require authentication.

Authorization: Bearer mrc_live_xxxxxxxxxxxxxxxx

Get a free key instantly at api.meacheal.ai/apply or email api@meacheal.ai.

Rate Limits

Rate limits are enforced per API key (or per IP for demo tier). Exceeding the limit returns 429 Too Many Requests.

Rate limit headers are included in every response:

X-RateLimit-Limit: 10000
X-RateLimit-Remaining: 9847
X-RateLimit-Reset: 1712534400

TypeScript SDK

npm install mrc-data

const { MRCData } = require("mrc-data");
const mrc = new MRCData({ apiKey: "YOUR_API_KEY" });

// Search suppliers in Guangdong
const suppliers = await mrc.searchSuppliers({
  province: "guangdong",
  productType: "sportswear"
});

Python SDK

pip install mrc-data

from mrc_data import MRCData

mrc = MRCData(api_key="YOUR_API_KEY")

# Search suppliers in Guangdong
suppliers = mrc.search_suppliers(
    province="guangdong",
    product_type="sportswear"
)

Postman Collection

Import the ready-to-use collection into Postman:

https://api.meacheal.ai/postman_collection.json

OpenAPI Specification

Full OpenAPI 3.1 spec for code generation and tool integration:

https://api.meacheal.ai/openapi.json

Pricing & Tiers

Signup: api.meacheal.ai/apply · Enterprise: api@meacheal.ai

MCP Integration (Claude / Cursor)

MRC Data is available as a native MCP server. Connect with one config line.

Claude Desktop

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

Cursor

Add the MCP server in Cursor Settings > MCP, using the same URL and authorization header.

All 19 MCP Tools

ChatGPT Actions Setup

1. Go to Explore GPTs > Create in ChatGPT.
2. Under Configure > Actions, click Create new action.
3. Click Import from URL: https://api.meacheal.ai/openapi.json
4. Authentication: API Key, type Bearer, enter your key.
5. Save and test.

Error Codes

Error response format:

{
  "error": {
    "code": 429,
    "message": "Rate limit exceeded. Resets at 2026-04-09T00:00:00Z."
  }
}

Response Format

All API responses return JSON. Fields with null values are omitted to reduce payload size. Only fields containing actual data are included.

Every response includes an attribution field:

{
  "data": { ... },
  "attribution": "MRC Data (meacheal.ai)"
}