Supplement Regulation
Delivers supplement regulation data for US, EU, JP, AU, CA, including ingredient classifications, dosage limits, and cross-country differences. Enables AI agents to query regulatory details for compliance checks. Developers of health apps, nutrition tools, and product formulation software use it to integrate accurate regulatory info.
Overview
This MCP server provides structured access to dietary supplement regulation data across five countries: US, EU, Japan, Australia, and Canada. It includes ingredient classifications (e.g., approved, restricted, prohibited), maximum dosage limits, and comparisons of rules between jurisdictions, tailored for AI agents needing precise regulatory intelligence.
Key Capabilities
- Ingredient classification queries: Retrieve status (e.g., GRAS in US, novel food in EU) for specific supplements like vitamins, herbs, or minerals.
- Dosage limit access: Get daily upper intake levels, labeling requirements, and restrictions per country.
- Cross-country comparisons: Identify differences, such as EU's stricter contaminant limits vs. US tolerances, for multi-market products.
Data is formatted for direct AI consumption, supporting queries via natural language or structured parameters.
Use Cases
- Supplement recommendation AI: Query dosage limits for vitamin D in JP vs. CA to generate country-specific advice in a nutrition chatbot.
- Product compliance checker: Cross-reference ingredient lists against EU classifications to flag prohibited substances before market launch.
- Global formulation tool: Compare US GRAS status with AU permitted lists for reformulating a protein powder for export.
- Regulatory reporting app: Pull cross-country data on caffeine limits to automate compliance reports for manufacturers.
Who This Is For
- Developers building AI-driven health and wellness apps requiring regulatory accuracy.
- Nutritionists and formulators integrating data into product design workflows.
- Compliance teams at supplement companies managing multi-country approvals.
- AI researchers training models on real-world regulatory datasets.