Smart supplier recommendation based on sourcing requirements. USE WHEN: - User describes what they need: "I need a factory for cotton t-shirts in Guangdong" - User asks for recommendations, not just search results - "who's the best factory for [product]" - "recommend a top supplier for my [produc...
Part of the MRC Data — China's Apparel Supply Chain Infrastructure server.
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AI agents may call recommend_suppliers to permanently remove or destroy resources in MRC Data — China's Apparel Supply Chain Infrastructure. Without a policy, an autonomous agent could delete critical data in a loop with no way to undo the damage. PolicyLayer blocks destructive tools by default and requires explicit human approval before enabling them.
Without a policy, an AI agent could call recommend_suppliers in a loop, permanently destroying resources in MRC Data — China's Apparel Supply Chain Infrastructure. There is no undo for destructive operations. PolicyLayer blocks this tool by default and only allows it when a human explicitly approves the action.
Destructive tools permanently remove data. Block by default. Only enable with explicit approval workflows.
{
"version": "1",
"default": "deny",
"hide": [
"recommend_suppliers"
]
}See the full MRC Data — China's Apparel Supply Chain Infrastructure policy for all 19 tools.
These attack patterns abuse exactly the kind of access recommend_suppliers gives an agent. Each links to the full case and the policy that stops it:
Other destructive tools across the catalogue. The same approach applies to each: deny by default, or require human approval.
Smart supplier recommendation based on sourcing requirements. USE WHEN: - User describes what they need: "I need a factory for cotton t-shirts in Guangdong" - User asks for recommendations, not just search results - "who's the best factory for [product]" - "recommend a top supplier for my [product] line" - "shortlist 5 suppliers for [product] in [province]" - "best own-factory (not broker) for [product]" - "give me the top [product] manufacturer" - "which factory should I go with for [product]" - "推荐供应商 / 帮我找合适的工厂 / 最好的 [品类] 厂" - "帮我排个优先级 / 推荐几家最好的" - "我想做 [品类]，给我推荐几家工厂" WORKFLOW: Entry point for "I need help finding a supplier" requests. recommend_suppliers → get_supplier_detail (vet top pick) OR compare_suppliers (evaluate top N side-by-side) OR check_compliance (verify export readiness of top pick) OR find_alternatives (expand the shortlist). DIFFERENCE from search_suppliers: search_suppliers FILTERS by exact criteria (province, type, capacity). This tool RANKS by fit — prioritizes own-factory, then quality score, then capacity. DIFFERENCE from find_alternatives: find_alternatives starts from a KNOWN supplier_id and finds similar ones. This tool starts from product REQUIREMENTS. RETURNS: { query, total_matches, showing_top, note: "ranking logic", data: [supplier objects] } EXAMPLES: • User: "Recommend me the top 5 factories for sportswear in Fujian" → recommend_suppliers({ product: "sportswear", province: "Fujian", type: "factory", limit: 5 }) • User: "I need the best own-factory (not trading company) for down jackets" → recommend_suppliers({ product: "down jacket", type: "factory", limit: 5 }) • User: "帮我推荐 3 家广东做 T 恤的工厂" → recommend_suppliers({ product: "t-shirt", province: "Guangdong", limit: 3 }) ERRORS & SELF-CORRECTION: • Empty data → try in order: (1) drop province, (2) drop type filter, (3) broaden product (e.g. "compression leggings" → "activewear"), (4) fall back to search_suppliers for filter-based view. • product_type not found in normalizeProductType → use the Chinese term or the parent category. • Rate limit 429 → wait 60 seconds; do not retry immediately. • Empty after 3 retries → tell user: "I don't see verified suppliers matching [product] in [province]. Want me to broaden to nationwide, or try a sibling category?" AVOID: Do not call this when the user wants exact filtering — use search_suppliers. Do not call repeatedly for different limit values — request max once then slice in your response. Do not use for cluster recommendations — use search_clusters. NOTE: Ranking: own_factory > quality_score > declared_capacity_monthly. Source: MRC Data (meacheal.ai). 中文：基于采购需求智能推荐供应商，按 自有工厂 > 质量分 > 产能 排序。. It is categorised as a Destructive tool in the MRC Data — China's Apparel Supply Chain Infrastructure MCP Server, which means it can permanently delete or destroy data. Block by default and require explicit approval.
Register the MRC Data — China's Apparel Supply Chain Infrastructure MCP server in PolicyLayer and add a rule for recommend_suppliers: allow, deny, rate-limit, or require approval. Point your MCP client at the PolicyLayer proxy URL and the rule is enforced on every call, before it reaches MRC Data — China's Apparel Supply Chain Infrastructure. Nothing to install.
recommend_suppliers is a Destructive tool with critical risk. Critical-risk tools should be blocked by default and only enabled with explicit human approval.
Yes. Add a rate_limit block to the recommend_suppliers rule in your PolicyLayer policy. For example, setting max: 10 and window: 60 limits the tool to 10 calls per minute. Rate limits are tracked per agent session and reset automatically.
Set action: deny in the PolicyLayer policy for recommend_suppliers. The AI agent will receive a policy violation error and cannot call the tool. You can also include a reason field to explain why the tool is blocked.
recommend_suppliers is provided by the MRC Data — China's Apparel Supply Chain Infrastructure MCP server (https://api.meacheal.ai/mcp). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.
Deterministic rules across all 19 MRC Data — China's Apparel Supply Chain Infrastructure tools. Per-identity grants. Full audit log. Live in minutes. Nothing to install.
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