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MRC Data — China's Apparel Supply Chain Infrastructure

Find Alternative Suppliers

find_alternatives
Read-onlyIdempotent

Find alternative suppliers similar to a given supplier.

USE WHEN:

  • User says "this supplier is too expensive / too slow / too far"

  • User needs backup options for an existing supplier

  • "give me backup options for sup_XXX"

  • "find 5 alternatives to [supplier] in a different province"

  • "we need a cheaper / faster / closer / higher-quality alternative to sup_XXX"

  • "diversify our supplier pool away from [supplier]"

  • "de-risk single-source on sup_XXX"

  • "follow-up after get_supplier_detail: 'who else could make this?'"

  • "有没有替代 / 找类似的 / 换一家 / 备选供应商 / 分散供应链"

  • "[供应商] 太贵了 / 太慢了,换一家"

  • "给我几个备用工厂 / 备选方案"

Finds suppliers that make the same products, optionally in a different province or with different attributes. Results exclude the original supplier.

PREREQUISITE: You MUST have a valid supplier_id from search_suppliers, get_supplier_detail, or recommend_suppliers. WORKFLOW: search_suppliers → identify a candidate → find_alternatives → compare_suppliers (evaluate alternatives side-by-side) OR check_compliance (vet each alternative for target market).

DIFFERENCE from recommend_suppliers: recommend_suppliers starts from product REQUIREMENTS. This tool starts from a KNOWN supplier_id and finds similar alternatives. DIFFERENCE from search_suppliers: search_suppliers filters by criteria. This tool uses an existing supplier as the baseline reference.

RETURNS: { original_supplier, reason, alternatives: [supplier summaries], attribution }

EXAMPLES: • User: "sup_001 is too slow. Find 5 faster alternatives" → find_alternatives({ supplier_id: "sup_001", reason: "faster", limit: 5 }) • User: "Give me cheaper backup options for sup_042 in Zhejiang" → find_alternatives({ supplier_id: "sup_042", reason: "cheaper", province: "Zhejiang", limit: 5 }) • User: "sup_123 质量不行,推荐几家质量更好的" → find_alternatives({ supplier_id: "sup_123", reason: "better_quality", limit: 5 })

ERRORS & SELF-CORRECTION: • "Supplier not found" → supplier_id invalid. Re-run search_suppliers. • "Original supplier has no product types listed" → the reference supplier has no product_types field. Use recommend_suppliers with the product category the user actually wants instead. • Empty alternatives → the product type is rare OR province filter is too narrow. Drop province filter first, then try broader product search via recommend_suppliers. • Rate limit 429 → wait 60 seconds; do not retry immediately.

AVOID: Do not call this without first knowing the user's complaint (cheaper/faster/closer/quality) — without reason, results are generic. Do not call to find a supplier from scratch — use recommend_suppliers or search_suppliers. Do not compare via this tool — use compare_suppliers after.

CONSTRAINT: Max 10 alternatives per call. Query matches up to 3 product types from the reference supplier.

NOTE: Source: MRC Data (meacheal.ai). Sorting: "faster" uses lead_time_days.bulk_min ASC; others use quality_score DESC.

中文:基于已知 supplier_id 查找同品类的备选供应商(支持按 便宜/快/近/质量 排序,可限定省份)。

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNoNumber of top results to return (1-10, default 5)
reasonNoWhy looking for alternativesany
provinceNoPreferred province for alternatives
supplier_idYesCurrent supplier ID to find alternatives for
verbose_hintsNoIf true, response includes _interpretation annotations explaining what the data means and _guidance on how to use it

TDQS

A4.8/5.0
Behavior5/5

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

Beyond annotations (readOnlyHint, idempotentHint, destructiveHint), the description details sorting behavior, error handling and self-correction steps, rate limit handling, constraints (max 10 alternatives), and return structure. This fully informs the agent of the tool's behavior and side effects.

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 long but well-structured with clear sections (USE WHEN, PREREQUISITE, WORKFLOW, DIFFERENCES, etc.). It is front-loaded with the core purpose and use cases. Every section adds necessary information, and the structure aids quick scanning. Slight redundancy in examples could be trimmed, but it remains effective.

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 the tool's complexity (5 parameters, 1 required, with enums and constraints), the description fully explains functionality, usage scenarios, error conditions, and output structure. It compensates for the lack of an output schema by describing the return format. The inclusion of a Chinese version also broadens accessibility.

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

Parameters4/5

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

The input schema already covers all parameters with descriptions. The description adds value by providing usage examples for each parameter (e.g., reason, province, limit) and explaining the verbose_hints parameter. While schema coverage is 100%, the examples and context elevate the understanding beyond the schema alone.

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: 'Find alternative suppliers similar to a given supplier.' It provides extensive use cases and explicitly distinguishes this tool from siblings like recommend_suppliers and search_suppliers, making its unique role unmistakable.

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 a 'USE WHEN' list with concrete examples, prerequisites (must have a valid supplier_id), workflow guidance, and explicit differences from similar tools. It also warns against incorrect usage in the 'AVOID' section, providing comprehensive decision support for the agent.

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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