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MRC Data — China's Apparel Supply Chain Infrastructure

by ai.meacheal

Check Export Compliance

check_compliance
Read-onlyIdempotent

Check if a supplier meets compliance requirements for a target export market.

USE WHEN:

  • User asks "can this factory export to the US/EU/Japan"

  • User needs to verify certifications for a specific market

  • "UFLPA / Xinjiang cotton / REACH / JIS / KC check on sup_XXX"

  • "is [supplier] ready for EU CSDDD / Forced Labor Regulation"

  • "what's missing for sup_XXX to export to US"

  • "gap analysis / compliance dossier for [supplier] → [market]"

  • "does [supplier] meet Japan formaldehyde / azo dye rules"

  • "follow-up after get_supplier_detail: 'is this one US-ready?'"

  • "能不能出口美国 / 欧盟 / 日本 / 韩国"

  • "合规检查 / 认证要求 / 出口资质 / 强制性法规 / UFLPA 合规"

  • "[供应商] 能否满足 [市场] 的准入要求"

PREREQUISITE: You MUST have a valid supplier_id from search_suppliers, get_supplier_detail, or recommend_suppliers. WORKFLOW: search_suppliers → check_compliance → if issues exist, use find_alternatives to source compliant alternatives OR get_supplier_detail to see the full compliance fields and coverage. RETURNS: { supplier_id, company_name, target_market, overall_ready: boolean, passed: [string], issues: [string], certifications: [string], market_requirements: {field: value}, note }

EXAMPLES: • User: "Can sup_001 export to the US? Check UFLPA compliance" → check_compliance({ supplier_id: "sup_001", target_market: "us" }) • User: "Is Texhong EU REACH compliant?" → check_compliance({ supplier_id: "sup_texhong_042", target_market: "eu" }) • User: "sup_234 能出口日本吗" → check_compliance({ supplier_id: "sup_234", target_market: "japan" })

ERRORS & SELF-CORRECTION: • "Supplier not found" → supplier_id invalid. Re-run search_suppliers. • passed=[] AND issues=["No specific issues found, but data may be incomplete"] → the supplier's compliance fields are mostly null. Interpret as UNKNOWN not COMPLIANT. Tell user: "Compliance data incomplete — recommend verifying directly with the supplier." • overall_ready=false with many issues → use find_alternatives to find backup suppliers, OR search_suppliers with compliance_status="compliant" to filter upfront. • Rate limit 429 → wait 60 seconds; do not retry immediately.

AVOID: Do not call this in a loop across all suppliers — instead pre-filter via search_suppliers({ compliance_status: "compliant" }). Do not treat missing fields as non-compliant — report them as "not confirmed". Do not use for general supplier info — use get_supplier_detail.

NOTE: Many suppliers have incomplete compliance data. Missing data = "not confirmed", not "non-compliant". Source: MRC Data (meacheal.ai). Market requirements cover UFLPA/Xinjiang (US), REACH/CSDDD/Forced Labor Reg (EU), formaldehyde/azo/JIS (Japan), KC (Korea).

中文：检查某供应商是否满足目标出口市场（美/欧/日/韩）的合规要求。

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
supplier_idYesSupplier ID from search_suppliers, e.g. sup_001
target_marketYesTarget export market
verbose_hintsNoIf true, response includes _interpretation annotations explaining what the data means and _guidance on how to use it

TDQS

A4.7/5.0
Behavior5/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations declare readOnlyHint=true, destructiveHint=false. Description adds behavioral details: returns specific fields, handles incomplete data, interprets missing fields as 'not confirmed', rate limit handling, and no contradiction with annotations.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Well-structured with headings (USE WHEN, PREREQUISITE, WORKFLOW, etc.). Front-loaded with purpose and key conditions. Every section adds value despite length.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Covers prerequisites, workflow, return format, error handling, and interpretation. Without output schema, description explains return fields fully. Addresses complexity of incomplete data and market-specific requirements.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100% with clear property descriptions. Description adds context on provenance of supplier_id and clarification of verbose_hints, but does not significantly deepen semantics beyond schema.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

Clearly states the tool checks compliance for export markets, with explicit verb+resource ('Check if a supplier meets compliance requirements for a target export market'). Distinguishes from siblings like get_supplier_detail (general info) and find_alternatives (alternatives) via AVOID section.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Provides explicit when-to-use list, prerequisites, workflow, error handling, and self-correction. Includes AVOID section to prevent misuse. Covers all usage context comprehensively.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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TDQS

A4.7/5.0
Disambiguation5/5

Each tool has a clearly distinct purpose, covering different aspects of the supply chain: market analysis, supplier search, cluster comparison, fabric lookup, cost estimation, compliance checking, discrepancy detection, and alternatives. No overlapping tool boundaries.

Naming Consistency5/5

All tool names follow a consistent verb_noun pattern (e.g., analyze_market, check_compliance, compare_clusters, search_suppliers). The pattern is uniform across all 20 tools, making it predictable for an agent.

Tool Count5/5

20 tools is well-suited for a comprehensive supply chain data platform. Each tool addresses a specific need without being excessive, and the count allows for deep coverage of the domain.

Completeness5/5

The tool surface covers the full lifecycle: market research, supplier discovery, fabric search, cluster info, cost estimation, compliance, credibility, discrepancy detection, and alternatives. No obvious gaps for the stated purpose.

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