meacheal-ai/mrc-data
by Various
China's apparel supply chain data infrastructure for AI agents — 3,000+ verified suppliers, 350+ lab-tested fabrics, 170+ industrial clusters. MCP + REST + OpenAPI.
MCP
meacheal-ai/mrc-data
Added 1 June 2026
Overview
A data infrastructure tool that provides verified supplier and fabric data for AI agents operating in China's apparel supply chain. It exposes 3,000+ suppliers, 350+ lab-tested fabrics, and 170+ industrial clusters through MCP, REST, and OpenAPI interfaces.
Best for
Best for
Developers building AI agents for China's apparel sourcing and supply chain management
Use cases
- Integrate verified supplier data into AI-driven sourcing agents
- Query lab-tested fabric specifications programmatically
- Map industrial clusters for supply chain optimization
Notes
A data infrastructure tool that provides verified supplier and fabric data for AI agents operating in China’s apparel supply chain. It exposes 3,000+ suppliers, 350+ lab-tested fabrics, and 170+ industrial clusters through MCP, REST, and OpenAPI interfaces.
1 stars on GitHub. Last updated 2026-04-19.
Use cases
- Integrate verified supplier data into AI-driven sourcing agents
- Query lab-tested fabric specifications programmatically
- Map industrial clusters for supply chain optimization
Pros
- Large, curated dataset of verified suppliers and fabrics
- Multiple API interfaces (MCP, REST, OpenAPI) for flexible integration
- Covers a specific, high-value niche in apparel supply chain
Cons
- Very early stage with only 1 GitHub star and limited community adoption
- Documentation and usage examples are minimal
- Shell-based implementation may limit cross-platform ease of use
Indexed from awesome-mcp-servers-punkpeye and enriched against its public facts.
Pros
- Large, curated dataset of verified suppliers and fabrics
- Multiple API interfaces (MCP, REST, OpenAPI) for flexible integration
- Covers a specific, high-value niche in apparel supply chain
Cons
- Very early stage with only 1 GitHub star and limited community adoption
- Documentation and usage examples are minimal
- Shell-based implementation may limit cross-platform ease of use
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