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MRC Data — China's Apparel Supply Chain Infrastructure

by ai.meacheal

Detect Spec Discrepancies

detect_discrepancy
Read-onlyIdempotent

[Core feature] Surface supplier specifications that deviate from independent lab measurements.

USE WHEN user asks:

  • "which fabrics have lab-test deviations on weight"

  • "find suppliers whose stated capacity differs from on-site measurements"

  • "compare cotton content lab results across suppliers"

  • "which suppliers have the closest match between specs and lab tests"

  • "show me suppliers with >20% capacity over-reporting"

  • "which factories inflate worker count"

  • "audit integrity check on our supplier pool"

  • "follow-up: 'are any of these suppliers flagged for discrepancy?'"

  • "data integrity / quality audit / spec validation"

  • "实测数据 / 数据可信度 / 规格与实测偏差 / 虚报产能 / 成分不符"

  • "哪些供应商产能造假 / 数据不准"

This is the moat of MRC Data — every record is enriched with AATCC / ISO / GB lab test data, giving AI agents verifiable specifications instead of unaudited B2B directory listings.

Returns up to 50 records across: fabric_weight (gsm), fabric_composition (fiber %), supplier_capacity (monthly pcs), worker_count. Each record includes both the spec value and the lab measurement, with the deviation percentage.

WORKFLOW: Standalone audit tool — does not require prior search. Call directly with field type and threshold. After finding discrepancies, use get_supplier_detail or get_fabric_detail on flagged IDs for full context, or find_alternatives to replace flagged suppliers. RETURNS: { field, min_discrepancy_pct, count, data: [{ id, name, declared_value, tested_value, discrepancy_pct }] }

EXAMPLES: • User: "Which fabrics have more than 10% weight deviation from their spec sheets?" → detect_discrepancy({ field: "fabric_weight", min_discrepancy_pct: 10 }) • User: "Find suppliers whose declared monthly capacity is >25% off from verified measurements" → detect_discrepancy({ field: "supplier_capacity", min_discrepancy_pct: 25 }) • User: "哪些面料的成分跟实测不一样" → detect_discrepancy({ field: "fabric_composition" }) — composition is exact-match, no threshold

ERRORS & SELF-CORRECTION: • count=0 → no records above threshold. Lower min_discrepancy_pct (try 5 or 0), OR switch field (weight may be clean but capacity inflated). • Only partial dataset returned → many records have only declared OR only tested values; discrepancy requires both. This is a data coverage limit, not a bug. • Rate limit 429 → wait 60 seconds; do not retry immediately.

AVOID: Do not present discrepancy data as proof of fraud — call it out as "declared vs lab-measured delta". Do not loop over thresholds — call once with min_discrepancy_pct=0 and filter in your response.

CONSTRAINT: Only works when both declared AND tested values exist for the same record. Many records have only one or the other. Max 50 records per call.

NOTE: Source: MRC Data (meacheal.ai). Methods: AATCC / ISO / GB per field.

中文：识别供应商规格与实测值偏差较大的记录。返回规格值、实测值、偏差百分比。

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
fieldYesType of discrepancy to detect: fabric_weight (面料克重) / fabric_composition (成分) / supplier_capacity (产能) / worker_count (工人数)
verbose_hintsNoIf true, response includes _interpretation annotations explaining what the data means and _guidance on how to use it
min_discrepancy_pctNoMinimum discrepancy threshold as percentage (e.g. 10 = only show ≥10% mismatch)

TDQS

A4.7/5.0
Behavior4/5

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

Annotations already declare readOnlyHint, idempotentHint, and destructiveHint=false. Description adds valuable context: returns up to 50 records, requires both declared and tested values, handles count=0 by lowering threshold, mentions rate limit (429). While thorough, it could be slightly more concise.

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

Conciseness4/5

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

Description is very detailed with clear sections but somewhat verbose; could be trimmed without losing clarity. However, it is well-structured with core feature upfront and organized sections.

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?

Given no output schema, the description fully covers return format (JSON structure with fields), error scenarios (count=0, partial data, rate limit), parameter usage, and workflow. It is complete for an AI agent to correctly select and invoke the tool.

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

Parameters5/5

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

Input schema has 100% coverage; description enhances semantics with usage examples, clarifies that composition uses exact-match, explains min_discrepancy_pct default (0), and describes verbose_hints behavior. Adds significant value 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?

Description clearly states the tool surfaces deviations between supplier specifications and independent lab measurements, with specific fields listed. It distinguishes itself from sibling tools by emphasizing its role as an audit tool leveraging proprietary lab data.

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?

Explicitly enumerates user query patterns in both English and Chinese, provides workflow guidance (e.g., after discrepancies use get_supplier_detail), and instructs what to avoid (e.g., presenting as fraud, looping over thresholds).

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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