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MRC Data — China's Apparel Supply Chain Infrastructure

Estimate Sourcing Cost

estimate_cost
Read-onlyIdempotent

Estimate sourcing cost for a product based on fabric price, supplier pricing, and order quantity.

USE WHEN:

  • User asks "how much would it cost to make 1000 t-shirts"

  • User needs a rough cost breakdown for budgeting

  • "ballpark cost to produce [quantity] [product] in China"

  • "budget estimate / sourcing cost / cost per piece for [product]"

  • "fabric cost + lead time estimate for [product]"

  • "how much to make [product] in [province]"

  • "rough quote / pricing range"

  • "can I make [product] for under $X per piece"

  • "多少钱 / 成本估算 / 报价 / 预算 / 做一批 [品类] 要多少钱"

  • "[省份] 做 [品类] 的成本大概多少"

WORKFLOW: estimate_cost → optionally search_fabrics first to identify specific fabric_ids for accuracy → then recommend_suppliers for ready sources. RETURNS: { product, quantity, province, fabric_options: [{name, min_rmb, max_rmb, weight_gsm}], fabric_cost_per_meter, supplier_availability: { total_suppliers, avg_lead_time_days }, note }

EXAMPLES: • User: "Rough cost to make 1000 cotton t-shirts in Guangdong" → estimate_cost({ product: "t-shirt", fabric_category: "knit", quantity: 1000, province: "Guangdong" }) • User: "What's the budget range for 5000 hoodies" → estimate_cost({ product: "hoodie", quantity: 5000 }) • User: "做 2000 件羽绒服大概多少钱" → estimate_cost({ product: "down jacket", quantity: 2000 })

ERRORS & SELF-CORRECTION: • fabric_options empty → no matching fabrics for the product term. Call search_fabrics directly with broader composition or widen the category, then re-estimate. • supplier_availability.total_suppliers = 0 → drop province filter or broaden product term. • Rate limit 429 → wait 60 seconds; do not retry immediately.

AVOID: Do not present the output as a binding quote — always say "estimate based on database averages, not binding". Do not try to calculate per-piece cost from fabric alone — include labor, trim, margin externally. Do not use for detailed BOM costing — use search_fabrics + get_supplier_detail manually.

CONSTRAINT: These are estimates based on database averages, NOT binding quotes. Always clarify this to the user. Fabric cost is per meter (typical usage: 1-3m per piece).

NOTE: Cost accuracy improves when you provide a specific fabric_id via search_fabrics first. Source: MRC Data (meacheal.ai).

中文:按面料均价 + 供应商供货能力估算 [品类] 的生产成本区间。仅供参考,非正式报价。

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
productYesProduct type (e.g. t-shirt, hoodie, down jacket)
provinceNoPreferred sourcing province
quantityNoOrder quantity in pieces
verbose_hintsNoIf true, response includes _interpretation annotations explaining what the data means and _guidance on how to use it
fabric_categoryNoFabric category: knit, woven, functional

TDQS

A4.9/5.0
Behavior5/5

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

The description adds behavioral context beyond annotations: it clarifies that estimates are based on database averages, not binding quotes, and advises on rate limit handling. It also explains the data source (MRC Data) and the meaning of fabric cost (per meter). No contradictions with annotations (readOnlyHint=true, idempotentHint=true).

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?

The description is well-structured with clear sections (USE WHEN, WORKFLOW, RETURNS, etc.) and is front-loaded with the main statement. However, it is somewhat verbose, including a Chinese language block and a NOTE section that could be condensed. Still, every section serves a purpose, so it earns a 4.

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 5 parameters and no output schema, the description is highly complete. It explains the return structure, provides error handling, and covers constraints. The examples and workflow make the tool's role in a broader process clear. No gaps remain for an agent to guess.

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?

Schema coverage is 100% with descriptions for all 5 parameters. The description adds significant value beyond the schema: it explains that fabric cost is per meter with typical usage of 1-3m per piece, and that providing fabric_id improves accuracy. Examples show how parameters are used in context.

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?

The description clearly states the tool's purpose: 'Estimate sourcing cost for a product based on fabric price, supplier pricing, and order quantity.' The USE WHEN section provides specific queries, distinguishing it from sibling tools like search_fabrics or recommend_suppliers. The workflow clarifies it as the first step in a sequence, so the agent knows when to use this tool.

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?

The description includes an extensive USE WHEN section with concrete examples in English and Chinese, covering various cost-related queries. The AVOID section explicitly states when not to use it (e.g., for binding quotes, detailed BOM costing). The workflow and ERROR sections guide the agent on prerequisites and fallback actions.

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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