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MRC Data — China's Apparel Supply Chain Infrastructure

by ai.meacheal

Get Database Stats

get_stats
Read-onlyIdempotent

Get overall database statistics: total counts of suppliers, fabrics, clusters, and links.

USE WHEN user asks:

  • "how big is your database" / "what's the coverage" / "data overview"

  • "how many suppliers / fabrics / clusters do you have"

  • "database size / scale / freshness"

  • "is the data up to date"

  • "live counts for MRC data"

  • "first-time onboarding: 'what can MRC data do for me'"

  • "数据库多大 / 有多少数据 / 覆盖多少供应商"

  • "你们的数据规模 / 数据量 / 新鲜度"

WORKFLOW: Standalone discovery tool — call this first when a user asks about data scale or freshness. Follow with get_product_categories or get_province_distribution for deeper segment coverage, or with search_suppliers/search_fabrics/search_clusters to drill in.

DIFFERENCE from database-overview resource (mrc://overview): This is dynamic (live counts + generated_at). The resource is static (geographic scope, top provinces, data standards).

RETURNS: { database, generated_at, tables: { suppliers: { total }, fabrics: { total }, clusters: { total }, supplier_fabrics: { total } }, attribution }

EXAMPLES: • User: "How big is the MRC database?" → get_stats({}) • User: "Give me the latest data scale numbers" → get_stats({}) • User: "MRC 数据库有多少供应商和面料" → get_stats({})

ERRORS & SELF-CORRECTION: • All counts 0 → database query failed or D1 binding lost. Retry once after 5 seconds. If still 0, surface a transport error to user. • Rate limit 429 → wait 60 seconds; do not retry immediately.

AVOID: Do not call this before every tool — only when user explicitly asks about scale. Do not call to get per-category counts — use get_product_categories. Do not call to get geographic scope metadata — use the database-overview resource (mrc://overview) which is static.

NOTE: Only reports verified + partially_verified records. Unverified reserve data is excluded from counts. Source: MRC Data (meacheal.ai).

中文：获取数据库整体统计（供应商总数、面料总数、产业带总数、关联记录数）。动态快照，含生成时间戳。

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
verbose_hintsNoIf true, response includes _interpretation annotations explaining what the data means and _guidance on how to use it

TDQS

A4.6/5.0
Behavior5/5

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

Annotations already mark readOnlyHint true, idempotentHint true, destructiveHint false. The description adds significant behavioral context: it returns a dynamic snapshot with generated_at timestamp, excludes unverified records, explains error handling (all zeros indicates failure, rate limiting), and notes data source. 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.

Conciseness4/5

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

The description is comprehensive but well-structured with clear sections (USE WHEN, WORKFLOW, DIFFERENCE, RETURNS, etc.). It is lengthy but every section adds value. Could be slightly more concise, but overall efficient for the information conveyed.

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?

Despite no output schema, the description includes the return shape, examples, and error handling. The tool has low complexity (1 optional param) and the description covers all necessary context for an agent to invoke it correctly.

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 a single optional boolean parameter 'verbose_hints'. The description does not add meaning beyond the schema's description of the parameter. Baseline score of 3 is appropriate.

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 returns overall database statistics (suppliers, fabrics, clusters, links). The verb 'Get' and resource 'Database Stats' are specific. It explicitly distinguishes from siblings like get_product_categories and get_province_distribution, and from the static resource mrc://overview.

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 extensive use case scenarios with example user queries in English and Chinese. Includes workflow guidance (call this first when asking about scale), explicit differences from similar tools, and a clear 'AVOID' section detailing when not to use this tool and which alternatives to use instead.

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.

Resources