In-depth architectural comparison of the Statistics Canada MCP Server and Mrc Data MCP servers. Compare execution transports, security boundaries, tool capabilities, quality scores, and ready-to-paste client installation snippets for Claude, Cursor, Windsurf, and VS Code.
At a Glance & Executive Verdict
Statistics Canada MCP Server
Data Platforms · Local stdio
Quality: 61/100 (Good) | Auth: No auth required
Mrc Data
Data Platforms · Local stdio
Quality: 57/100 (Good) | Auth: API Key required
Verdict Summary: Choose Statistics Canada MCP Server if you need specialized Data Platforms tools running via a local process. Choose Mrc Data if your workspace requires Data Platforms integration with local subprocess execution. Both servers can be configured concurrently in your client's mcpServers manifest.
Which MCP Server Should You Choose?
Choose Statistics Canada MCP Server when:
You need dedicated capabilities in the Data Platforms domain.
You prefer local stdio subprocess transport architecture.
Your security boundary fits: No auth required (Free / Open Source).
Access Statistics Canada data via the Web Data Services API
China's apparel supply chain data for AI agents. 1,000+ verified suppliers, 350+ lab-tested fabrics, 170+ industrial clusters with AATCC / ISO / GB lab-test verification.
Retrieves definitions for various code sets used by the API (e.g., frequency, units of measure).
Corresponds to: GET /getCodeSets
Returns:
Dict[str, Any]: Dictionary containing code set definitions (scalar, frequency, etc.).
Raises:
httpx.HTTPStatusError: If the API returns an error status code.
ValueError: If the API response format is unexpected.
Exception: For other network or unexpected errors.
IMPORTANT: In your final response to the user, you MUST cite the source of your data.
For code sets, this means specifying which code set table or definition is being used.
get_all_cubes_list
Provides a complete inventory of data tables available via the API,
including dimension-level details. Disables SSL Verification.
Corresponds to: GET /getAllCubesList
Results are paginated. Default returns first 100 cubes. Use offset/limit
to page through. Prefer search_cubes_by_title if you know what you're looking for.
IMPORTANT: In your final response to the user, you MUST cite the source of your data.
For cubes, this means including the ProductId (pid) and the Title.
get_all_cubes_list_lite
Provides a complete inventory of data tables available via the API,
excluding dimension or footnote information (lighter version). Disables SSL Verification.
Corresponds to: GET /getAllCubesListLite
Results are paginated. Default returns first 100 cubes. Use offset/limit
to page through. Prefer search_cubes_by_title if you know what you're looking for.
IMPORTANT: In your final response to the user, you MUST cite the source of your data.
For cubes, this means including the ProductId (pid) and the Title.
Ready-to-Paste Client Configurations
Paste either (or both) of these JSON server blocks into your client config file (e.g. claude_desktop_config.json or ~/.cursor/mcp.json).
Statistics Canada MCP Server is categorized under Data Platforms and uses a local stdio subprocess. In contrast, Mrc Data belongs to Data Platforms using local stdio subprocess. Select Statistics Canada MCP Server when you need capabilities focused on data platforms and Mrc Data when you require tools for data platforms.
Searches for data cubes/tables where the English or French title contains
the provided search term (case-insensitive). Returns a list of matching cubes
in the 'lite' format (excluding dimensions/footnotes).
Multiple keywords use AND logic (e.g., "tobacco smoking age" finds cubes
containing ALL three words). Results are capped at max_results (default 25).
IMPORTANT: In your final response to the user, you MUST cite the source of your data.
For cubes, this means including the ProductId (pid) and the Title.
Raises:
httpx.HTTPStatusError: If the underlying API call fails.
Exception: For other network or unexpected errors during the fetch.
get_cube_metadata
Retrieves detailed metadata for a specific data table/cube using its ProductId.
Includes dimension info, titles, date ranges, codes, etc. Disables SSL Verification.
Corresponds to: POST /getCubeMetadata
Start with summary=True (default). The summary strips noise (French translations,
archive codes, footnotes) and shows only 3 sample members per dimension with
_next_steps guidance. Safe for all context window sizes.
Set summary=False only if you need the full raw member list or all API fields.
To browse dimension codes for get_sdmx_data key construction, use get_sdmx_structure.
To resolve a coordinate to a vectorId, use get_series_info.
Returns:
Dict[str, Any]: The metadata object for the specified cube on success.
Raises:
httpx.HTTPStatusError: If the API returns an error status code.
ValueError: If the API response format is unexpected or status is not SUCCESS.
Exception: For other network or unexpected errors.
IMPORTANT: In your final response to the user, you MUST cite the source of your data.
For cubes, this means including the ProductId (pid) and the Title.
get_changed_series_data_from_cube_pid_coord
Retrieves changed series data (data points that have changed) using Cube ProductId
and Coordinate string.
Coordinates are automatically padded to 10 dimensions. Disables SSL Verification.
Corresponds to: POST /getChangedSeriesDataFromCubePidCoord
Returns:
Dict[str, Any]: A dictionary containing the changed series data object.
Raises:
httpx.HTTPStatusError: If the API returns an error status code.
ValueError: If the API response format is unexpected or status is not SUCCESS.
Exception: For other network or unexpected errors.
IMPORTANT: In your final response to the user, you MUST cite the source of your data.
For changed series data, this means including the VectorId, ProductId (pid), and Coordinate.
get_series_info
Resolve one or more {productId, coordinate} pairs to series metadata
(vectorId, titles, frequency, UOM, etc.) in a single API call.
Use this to find vectorIds before fetching data with get_sdmx_data or
get_sdmx_vector_data. Pass one item or many — same tool either way.
Coordinates are automatically padded to 10 dimensions.
Corresponds to: POST /getSeriesInfoFromCubePidCoord (accepts array)
NOTE: Response fields like scalarFactorCode, frequencyCode, and memberUomCode
use StatCan numeric codes. Call get_code_sets() to decode them
(e.g. frequencyCode 6 = "Monthly", scalarFactorCode 0 = "Units").
Returns:
List of series metadata dicts, paginated with _guidance if >50 results.
Raises:
httpx.HTTPStatusError: If the API returns an error status code.
ValueError: If no items return SUCCESS.
Exception: For other network or unexpected errors.
IMPORTANT: In your final response cite the ProductId and Coordinate for each series.
get_changed_cube_list
Get the list of data tables/cubes that were updated on a specific date (YYYY-MM-DD).
Disables SSL Verification.
Corresponds to: GET /getChangedCubeList/{date}
Returns:
List[Dict[str, Any]]: A list of dictionaries describing changed cube objects.
IMPORTANT: In your final response to the user, you MUST cite the source of your data.
For changed cubes, this means including the ProductId (pid) and Title.
get_series_info_from_vector
Request series metadata (productId, coordinate, titles, frequency, etc.)
by Vector ID. Disables SSL Verification.
Corresponds to: POST /getSeriesInfoFromVector
Returns:
Dict[str, Any]: A dictionary containing the series metadata object.
Raises:
httpx.HTTPStatusError: If the API returns an error status code.
ValueError: If the API response format is unexpected or status is not SUCCESS.
Exception: For other network or unexpected errors.
IMPORTANT: In your final response to the user, you MUST cite the source of your data.
For series info, this means including the VectorId, ProductId (pid), and Coordinate.
get_bulk_vector_data_by_range
Fetches bulk data for multiple vectors filtered by *release date* range (YYYY-MM-DDTHH:MM),
NOT by reference period. Use this when you want data released within a specific
date/time window (e.g., "all updates released yesterday").
*** IMPORTANT: release date vs reference period ***
- Use THIS tool when you want: "data released between date A and date B"
- Use get_sdmx_vector_data (startPeriod/endPeriod) when you want: "data for the time period YYYY to YYYY"
get_sdmx_vector_data is more reliable and filters by reference period, not release date.
*** LARGE RESPONSE WARNING ***
This tool can return hundreds of flattened data points. If the response
exceeds context limits, narrow the request: use fewer vectorIds, or use
offset/limit pagination to page through results in smaller chunks.
Response is pre-flattened: each element is one data point with vectorId,
productId, coordinate, and all value fields injected at the top level.
Disables SSL Verification.
Corresponds to: POST /getBulkVectorDataByRange
Returns:
List[Dict[str, Any]]: Flat list of data point dicts, each tagged with vectorId,
productId, and coordinate.
Raises:
httpx.HTTPStatusError: If the API returns an error status code.
ValueError: If the API response format is unexpected or no vectors return SUCCESS.
Exception: For other network or unexpected errors.
IMPORTANT: In your final response to the user, you MUST cite the source of your data.
For vector data, this means including the VectorId and Release Time.
get_changed_series_data_from_vector
Get changed series data (data points that have changed) for a series
identified by Vector ID. Disables SSL Verification.
Corresponds to: POST /getChangedSeriesDataFromVector
Returns:
Dict[str, Any]: A dictionary containing the changed series data object.
Raises:
httpx.HTTPStatusError: If the API returns an error status code.
ValueError: If the API response format is unexpected or status is not SUCCESS.
Exception: For other network or unexpected errors.
IMPORTANT: In your final response to the user, you MUST cite the source of your data.
For changed series data, this means including the VectorId.
get_changed_series_list
Get the list of series (vectorId, productId, coordinate, releaseTime)
that were updated on a specific date (YYYY-MM-DD).
Disables SSL Verification.
Corresponds to: GET /getChangedSeriesList/{date}
Returns:
List[Dict[str, Any]]: A list of dictionaries describing changed series objects.
Raises:
httpx.HTTPStatusError: If the API returns an error status code.
ValueError: If date format is invalid or API response format is unexpected.
Exception: For other network or unexpected errors.
IMPORTANT: In your final response to the user, you MUST cite the source of your data.
For changed series, this means including the VectorId.
+13 more tools listed on main page
Mrc Data Tools (20)
search_suppliers
Search verified Chinese apparel manufacturers, apparel factories, and clothing suppliers.
USE WHEN user asks:
- "find me a clothing manufacturer in China / Guangdong / Zhejiang"
- "who makes [t-shirts / suits / denim / activewear] in China"
- "I need a BSCI / OEKO-TEX certified apparel factory"
- "looking for OEM / ODM apparel supplier with MOQ < N"
- "find factories with production capacity > N pieces/month"
- "list factories that export to the US / EU / Japan"
- "show me trading companies in Yiwu / Shenzhen / Shanghai"
- "which suppliers in [province] make [product]" (follow-up drill-down)
- "give me another page of suppliers" (pagination via offset)
- "who can produce knit tops under 300 MOQ"
- "search by company name 新鑫 / Xinxin / Texhong"
- "find workshop-scale suppliers for small batch sampling"
- "搜供应商 / 找服装厂 / 找制衣厂 / 找代工厂 / 找外贸公司"
- "帮我在[省份]找[品类]工厂，产能至少 N 件/月"
Filters: province, city, factory type (factory/trading_company/workshop), product category,
minimum monthly capacity, compliance status, quality score. Returns paginated supplier list
with company name, location, monthly capacity (lab-verified), compliance, quality score.
WORKFLOW: Primary entry point for supplier discovery. search_suppliers → get_supplier_detail (for full 60+ field profile) OR compare_suppliers (side-by-side for up to 10 IDs) OR find_alternatives (diversify the pool) OR check_compliance (verify export readiness) OR get_supplier_fabrics (see their fabric catalog).
RETURNS: { has_more: boolean, available_dimensions: string[], data: [{ supplier_id, company_name_cn, company_name_en, type, province, city, product_types, quality_score, verified_dims: "5/8", coverage_pct }] }
EXAMPLES:
• User: "Find BSCI-certified denim factories in Guangdong with MOQ under 500"
→ search_suppliers({ province: "Guangdong", product_type: "denim", compliance_status: "compliant", limit: 10 })
• User: "Who makes activewear for Lululemon in China?"
→ search_suppliers({ product_type: "activewear" }) — then filter results by client brand in get_supplier_detail
• User: "我要在浙江找做牛仔的工厂，产能大于 10 万件"
→ search_suppliers({ province: "Zhejiang", product_type: "denim", min_capacity: 100000 })
• User: "Show me the next 10 trading companies in Yiwu"
→ search_suppliers({ city: "Yiwu", type: "trading_company", limit: 10, offset: 10 })
ERRORS & SELF-CORRECTION:
• Empty data array → try these in order: (1) remove min_capacity filter, (2) drop city but keep province, (3) broaden product_type to parent category (e.g. "denim" → "bottoms"), (4) drop compliance_status, (5) try recommend_suppliers for ranked fit.
• "Invalid province" → use English (Guangdong) or standard Chinese (广东). Supported: 31 mainland provinces + HK/Macau.
• product_type returns 0 → the TYPO_MAP normalizes common variants; try synonyms ("tee" → "t-shirt", "jeans" → "denim", "运动服" → "activewear").
• Rate limit 429 → wait 60 seconds. Do not retry immediately.
• Empty after 3 retries → tell user: "I couldn't find suppliers matching [criteria]. Would you like me to broaden the search?"
AVOID: Do not call this tool in a loop across provinces — call get_province_distribution first to see where supply is concentrated. Do not use this for ranked "best fit" recommendations — use recommend_suppliers. Do not fetch details by looping — use compare_suppliers with up to 10 IDs.
NOTE: Use this for FILTERING by exact criteria. For ranked recommendations based on sourcing needs, use recommend_suppliers instead. Source: MRC Data (meacheal.ai).
中文：搜索经过核查的中国服装供应商档案，按地区、类型、产能、品类、合规状态等筛选。
get_supplier_detail
Get the complete profile of a single Chinese apparel supplier by ID.
PREREQUISITE: You MUST first call search_suppliers or recommend_suppliers to obtain a valid supplier_id. Do not guess IDs.
USE WHEN user asks:
- "tell me more about [supplier]" / "show full details for sup_XXX"
- "what certifications does this factory hold"
- "what's their monthly capacity / worker count / equipment list"
- "can [supplier] export to US / EU / Japan / Korea"
- "give me the full profile / dossier / fact sheet for [supplier]"
- "how verified is this supplier's data" (returns coverage_pct + 8 dimensions)
- "what's their ownership type — own factory or broker"
- "show payment terms / lead time / sample turnaround for sup_XXX"
- "这家供应商具体情况 / 详细资料 / 工厂档案"
- "[供应商] 的合规 / 认证 / 出口资质"
Returns 60+ fields including: monthly capacity (lab-verified), equipment list, certifications (BSCI/OEKO-TEX/GRS/SA8000), ownership type (own factory vs subcontractor vs broker), market access (US/EU/JP/KR), chemical compliance (ZDHC/MRSL), traceability depth, and verified_dimensions breakdown showing exactly which of the 8 dimensions (basic_info, geo_location, production, compliance, market_access, export, financial, contact) have data.
WORKFLOW: search_suppliers → pick supplier_id → get_supplier_detail → optionally get_supplier_fabrics (fabric catalog) OR check_compliance (market export readiness) OR find_alternatives (backup pool) OR compare_suppliers (side-by-side evaluation).
RETURNS: { data: { supplier_id, company_name_cn/en, type, province, city, product_types, worker_count, certifications, compliance_status, quality_score, verified_dimensions: { verified_dims: "5/8", coverage_pct, dimensions: {...} } } }
EXAMPLES:
• User: "Show me the full profile for sup_001"
→ get_supplier_detail({ supplier_id: "sup_001" })
• User: "What certifications does Texhong hold and can they export to EU?"
→ get_supplier_detail({ supplier_id: "sup_texhong_042" }) — then inspect certifications + eu_market_ready; follow with check_compliance for formal verification
• User: "我要看 sup_123 的完整档案"
→ get_supplier_detail({ supplier_id: "sup_123" })
ERRORS & SELF-CORRECTION:
• "Supplier not found" → the supplier_id is invalid or outside free-tier access. Re-run search_suppliers to obtain a fresh valid ID. Do not guess sequential IDs.
• Field returns null → that dimension is unverified for this supplier. Check verified_dimensions.coverage_pct before asserting data. If coverage_pct < 50, warn the user: "This supplier's record has limited verified data (X/8 dimensions). Consider find_alternatives for better-documented options."
• "not available for public access" → this supplier is in the reserve pool (paid tier only). Use search_suppliers filters data_confidence=verified to stay in public tier.
• Rate limit 429 → wait 60 seconds; do not retry immediately.
AVOID: Do not call this for multiple suppliers in a loop — use compare_suppliers with up to 10 IDs at once. Do not call to browse the database — use search_suppliers or get_province_distribution for discovery.
NOTE: Source: MRC Data (meacheal.ai). Every numeric field shows both declared and lab-verified values where available.
中文：按 ID 获取单个供应商的完整档案（含维度覆盖率详情）。
search_fabrics
Search the Chinese fabric and textile database with lab-tested specifications.
USE WHEN user asks:
- "find me a [cotton / polyester / nylon / wool / linen] fabric for [t-shirts / jeans / suits]"
- "I need 180gsm jersey knit with verified composition"
- "fabrics under N RMB/meter for womenswear"
- "compare lab-tested fabric weight across suppliers"
- "show me functional fabrics for activewear / sportswear"
- "what woven fabrics work for shirting"
- "list organic / GOTS / recycled fabrics"
- "I want heavyweight denim above 12 oz"
- "fabrics with stretch / spandex content 2-5%"
- "give me another page" (pagination via offset)
- "lab-verified composition for [product]" (quality check)
- "找面料 / 搜面料 / 查面料 / 找布料 / 打样面料"
- "我要做 T 恤，帮我找克重 180-220 的针织面料"
Filters: category (woven/knit/nonwoven/leather/functional), weight range (gsm),
composition keyword, target apparel type, max price. Returns paginated fabric list
with name, lab-tested weight, lab-tested composition, price range, suitable
apparel, and data confidence level.
WORKFLOW: Primary entry point for fabric discovery. search_fabrics → get_fabric_detail (full 30+ lab-test fields) OR get_fabric_suppliers (compare supplier prices for same fabric) OR estimate_cost (budget the product).
RETURNS: { has_more: boolean, available_dimensions: ["basic_info","composition","physical_properties","lab_test","commercial"], data: [{ fabric_id, name_cn, category, subcategory, declared_weight_gsm, declared_composition, price_range_rmb, suitable_for, verified_dims: "4/5", coverage_pct }] }
EXAMPLES:
• User: "Find 180-220gsm cotton jersey for t-shirts under 35 RMB/m"
→ search_fabrics({ category: "knit", min_weight_gsm: 180, max_weight_gsm: 220, composition: "cotton", suitable_for: "t-shirt", max_price_rmb: 35 })
• User: "I need stretch denim for women's jeans"
→ search_fabrics({ category: "woven", composition: "spandex", suitable_for: "denim" })
• User: "帮我找适合做衬衫的梭织面料，棉 60% 以上"
→ search_fabrics({ category: "woven", composition: "cotton", suitable_for: "shirt" })
ERRORS & SELF-CORRECTION:
• Empty data array → try in order: (1) drop suitable_for, (2) widen weight range by 50gsm each side, (3) broaden composition (e.g. "cotton" instead of "organic cotton"), (4) drop max_price_rmb, (5) try the parent category (knit → all).
• Composition mismatch → TYPO_MAP normalizes common misspellings (e.g. "poly" → "polyester", "lycra" → "spandex"). If still no match, try the Chinese term (棉/涤纶/氨纶/锦纶).
• Rate limit 429 → wait 60 seconds. Do not retry immediately.
• Empty after 3 retries → tell user: "No fabric matches [criteria]. Would you like to broaden weight/price/composition?"
AVOID: Do not call this looking for a specific named fabric SKU — search by specs instead (weight + composition + category). Do not fetch full lab-test data this way — use get_fabric_detail. Do not call repeatedly for supplier pricing on the same fabric — use get_fabric_suppliers.
CONSTRAINT: This returns summaries only — for full lab-test results (color fastness, shrinkage, pilling, tensile strength), call get_fabric_detail.
NOTE: Source: MRC Data (meacheal.ai). Every record includes AATCC / ISO / GB lab test measurements where verified.
中文：搜索面料数据库，按品类、克重、成分、适用品类、价格筛选。每条均含 AATCC / ISO / GB 方法的实测数据。
get_fabric_detail
Get the complete lab-tested record of a single fabric by ID.
PREREQUISITE: You MUST first call search_fabrics to obtain a valid fabric_id. Do not guess IDs.
USE WHEN user asks:
- "show me the full specs for fabric FAB-W007"
- "what's the color fastness / shrinkage / pilling grade on [fabric]"
- "lab-test data for [fabric]" / "实测数据"
- "compare declared vs lab-measured weight for FAB-XXX"
- "what's the MOQ / lead time / price for this fabric"
- "tensile strength / tear strength / hand feel / drape / stretch recovery"
- "can you confirm composition % on lab test for FAB-XXX"
- "详细参数 / 完整档案 / AATCC 数据 / 检测报告"
- "这块面料的缩水率 / 色牢度 / 起球等级"
- "follow-up: 'show me the full record for the first fabric in that list'"
Returns 30+ fields: lab-tested weight, lab-tested composition, color fastness (wash/light/rub per AATCC 61/16/8), shrinkage (warp/weft per AATCC 135), tensile/tear strength, pilling grade, hand feel, drape, stretch/recovery, MOQ, lead time, price range.
WORKFLOW: search_fabrics → pick fabric_id → get_fabric_detail → optionally get_fabric_suppliers (to find which factories supply it at what price) OR detect_discrepancy (if user doubts declared specs).
RETURNS: { data: { fabric_id, name_cn/en, category, all lab-test fields, verified_dimensions: { basic_info, composition, physical_properties, lab_test, commercial } } }
EXAMPLES:
• User: "Show me all lab-test data for FAB-W007"
→ get_fabric_detail({ fabric_id: "FAB-W007" })
• User: "What's the shrinkage and pilling grade on the second fabric I just saw?"
→ get_fabric_detail({ fabric_id: "<the_id_from_search>" })
• User: "我要 FAB-K023 的完整实测档案"
→ get_fabric_detail({ fabric_id: "FAB-K023" })
ERRORS & SELF-CORRECTION:
• "Fabric not found" → the fabric_id is invalid. Re-run search_fabrics and use an ID from the fresh results.
• Field returns null → that test wasn't performed on this fabric. Check verified_dimensions.lab_test to see what IS tested before asserting anything.
• "not available" → unverified fabric in reserve pool. Filter search_fabrics for higher data_confidence.
• Rate limit 429 → wait 60 seconds; do not retry immediately.
AVOID: Do not call in a loop for multiple fabrics — if user wants to compare fabrics, present the search_fabrics summary list instead. Do not call to browse — use search_fabrics with filters.
NOTE: Source: MRC Data (meacheal.ai). AATCC/ISO/GB methods cited per field.
中文：按 ID 获取单个面料的完整��测档案（含 AATCC/ISO/GB 检测指标）。
search_clusters
Search Chinese apparel industrial clusters and textile markets.
USE WHEN user asks:
- "where is China's [denim / suit / women's wear / underwear] manufacturing concentrated"
- "what is the largest [silk / cashmere / down jacket] industrial cluster in China"
- "industrial cluster comparison Humen vs Shaoxing vs Haining vs Zhili"
- "recommend an industrial cluster for sourcing [product]"
- "where should I set up a sourcing office for [category]"
- "list mega clusters for [category]"
- "fabric markets in Zhejiang / Jiangsu"
- "accessories / trim / zipper / button markets in China"
- "which province dominates [category] exports"
- "follow-up: 'tell me more about Humen's cluster scale'"
- "服装产业带 / 面料市场 / 产业集群 / 纺织集群 / 辅料市场"
- "做 [品类] 应该去哪个产业带 / 集群推荐"
Famous clusters this database covers include: Humen (Guangdong, womenswear), Shaoxing
Keqiao (Zhejiang, fabric mega-market), Haining (Zhejiang, leather), Zhili (Zhejiang,
children's wear), Shengze (Jiangsu, silk), Shantou (Guangdong, underwear), Puning
(Guangdong, jeans), Jinjiang (Fujian, sportswear), and more.
Returns paginated cluster list with name, location, specialization, scale, supplier
count, average rent and labor cost, and key advantages/risks.
WORKFLOW: Cluster discovery entry point. search_clusters → compare_clusters (side-by-side up to 10 cluster_ids) OR get_cluster_suppliers (list factories in that cluster) OR analyze_market (broader market view).
RETURNS: { has_more: boolean, data: [{ cluster_id, name_cn, name_en, type, province, city, specialization, scale, supplier_count, labor_cost_avg_rmb }] }
EXAMPLES:
• User: "Where are the biggest denim clusters in China?"
→ search_clusters({ specialization: "denim", scale: "mega" })
• User: "Show me fabric markets in Zhejiang"
→ search_clusters({ province: "Zhejiang", type: "fabric_market" })
• User: "童装产业带有哪些"
→ search_clusters({ specialization: "童装" })
ERRORS & SELF-CORRECTION:
• Empty data array → try in order: (1) drop scale filter, (2) broaden specialization (e.g. "服装" instead of "牛仔"), (3) remove type, (4) remove province.
• Specialization mismatch → both Chinese and English work. Synonyms: sportswear/运动服, womenswear/女装, underwear/内衣, denim/牛仔.
• Rate limit 429 → wait 60 seconds; do not retry immediately.
• Empty after 3 retries → tell user: "No clusters match [criteria]. Try broader specialization or removing filters."
AVOID: Do not use this for specific factory search — use search_suppliers. Do not compare clusters by calling search_clusters twice — use compare_clusters with cluster_ids.
NOTE: Source: MRC Data (meacheal.ai). 170+ clusters mapped across 31 provinces.
中文：搜索中国服装产业带和面料市场。
compare_clusters
Compare multiple Chinese apparel industrial clusters side-by-side on key metrics.
PREREQUISITE: You MUST first call search_clusters to obtain valid cluster_ids. Do not guess IDs.
USE WHEN user asks:
- "compare Humen vs Shishi vs Jinjiang"
- "which cluster has lower labor cost — Humen or Dongguan"
- "side-by-side: Haining vs Xintang for denim"
- "evaluate 3 clusters for my sportswear line"
- "对比 [产业带1] 和 [产业带2]" / "哪个集群更适合 [品类]"
- "rank these clusters by supplier count"
- "which cluster has the highest scale for womenswear"
- "follow-up: 'now compare the top 3 clusters you just listed'"
Returns full records for each cluster so they can be compared on labor cost, rent, supplier count, scale, specializations, advantages, and risks.
WORKFLOW: search_clusters → collect cluster_ids → compare_clusters → optionally get_cluster_suppliers on the winner to list factories in that specific cluster.
RETURNS: { count: number, data: [full cluster objects with all fields] }
EXAMPLES:
• User: "Compare Humen, Shishi, and Jinjiang for sportswear sourcing"
→ compare_clusters({ cluster_ids: ["humen_women", "shishi_casual", "jinjiang_sportswear"] })
• User: "I want to evaluate Keqiao vs Zhili fabric markets"
→ compare_clusters({ cluster_ids: ["keqiao_fabric", "zhili_children"] })
• User: "对比虎门、石狮、晋江三个产业带"
→ compare_clusters({ cluster_ids: ["humen_women", "shishi_casual", "jinjiang_sportswear"] })
ERRORS & SELF-CORRECTION:
• "Too many IDs (>10)" → split into batches of 10 and aggregate results in your response.
• Fewer results than IDs sent → missing IDs were silently skipped (invalid cluster_id). Re-run search_clusters to verify IDs.
• Empty data → all IDs were invalid. Re-run search_clusters and try again with fresh IDs.
• Rate limit 429 → wait 60 seconds; do not retry immediately.
AVOID: Do not call with guessed cluster_ids — always resolve them via search_clusters first. Do not use to list factories in a cluster — use get_cluster_suppliers. Do not compare > 10 clusters in one call.
CONSTRAINT: Max 10 cluster IDs per call.
NOTE: Source: MRC Data (meacheal.ai).
中文：对比多个产业带的核心指标（最多 10 个）。
detect_discrepancy
[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。
get_supplier_fabrics
List all fabrics a specific supplier can provide, with quoted prices.
USE WHEN user asks:
- "what fabrics does [supplier name] have" / "what can this factory source for me"
- "show me the catalog of supplier sup_XXX"
- "what does this manufacturer offer"
- "what fabric options does sup_XXX quote for denim"
- "does [supplier] supply [fabric type]"
- "price list / fabric catalog / offering sheet for sup_XXX"
- "MOQ per fabric at this supplier"
- "follow-up: 'what fabrics can they supply?' after identifying a supplier"
- "[供应商] 能供应哪些面料 / 报价表 / 起订量"
Returns fabric records linked to the supplier with: fabric name, category, weight,
composition, and the supplier's quoted price + MOQ for that specific fabric.
PREREQUISITE: You MUST have a valid supplier_id from search_suppliers or get_supplier_detail.
WORKFLOW: search_suppliers → get_supplier_detail → get_supplier_fabrics → optionally get_fabric_detail (for lab-test data on a specific fabric) OR get_fabric_suppliers (cross-check price vs other suppliers for same fabric).
RETURNS: { supplier_id, count, data: [{ fabric_id, name_cn, category, weight, composition, price_rmb, moq }] }
EXAMPLES:
• User: "What fabrics does sup_texhong_042 offer?"
→ get_supplier_fabrics({ supplier_id: "sup_texhong_042" })
• User: "Show me the fabric catalog and MOQs for sup_001"
→ get_supplier_fabrics({ supplier_id: "sup_001" })
• User: "sup_234 能做哪些面料，报价多少"
→ get_supplier_fabrics({ supplier_id: "sup_234" })
ERRORS & SELF-CORRECTION:
• count=0 → this supplier has no linked fabric catalog in the database. Either (a) they don't self-source fabrics (CMT-only) — confirm via get_supplier_detail.ownership_type, or (b) their catalog is unmapped — use search_fabrics with their expected specialization instead.
• "Supplier not found" (implicit) → the supplier_id is invalid. Re-run search_suppliers.
• Rate limit 429 → wait 60 seconds; do not retry immediately.
AVOID: Do not call this for a general fabric search — use search_fabrics. Do not call to compare prices across suppliers for the SAME fabric — use get_fabric_suppliers instead.
NOTE: Source: MRC Data (meacheal.ai). Prices are supplier-quoted, not binding offers.
中文：查询某供应商能供应的所有面料及其报价、起订量。
get_fabric_suppliers
List all suppliers offering a specific fabric, sorted by quality score, with price comparison.
USE WHEN user asks:
- "who supplies fabric fab_XXX" / "where can I buy this fabric"
- "compare prices for [fabric] across suppliers"
- "best supplier for [fabric specification]"
- "which factory has the lowest price on FAB-XXX"
- "rank suppliers by quality for this fabric"
- "follow-up: 'who else sells this?'"
- "source comparison for [fabric]"
- "price spread on FAB-XXX"
- "谁家有这块面料 / 哪个厂报价最低 / 面料供应商对比"
- "[面料] 有哪些供应商 / 货源"
Returns supplier records linked to the fabric with: company name, location, quality
score, and that supplier's quoted price + MOQ for the fabric. Sorted by supplier
quality score so the most reliable options appear first.
PREREQUISITE: You MUST have a valid fabric_id from search_fabrics.
WORKFLOW: search_fabrics → pick fabric_id → get_fabric_suppliers → optionally get_supplier_detail (vet the top-ranked supplier) OR compare_suppliers (up to 10 IDs from this list).
RETURNS: { fabric_id, count, data: [{ supplier_id, company_name_cn, province, city, quality_score, price_rmb, moq }] }
EXAMPLES:
• User: "Who supplies FAB-W007 and at what price?"
�� get_fabric_suppliers({ fabric_id: "FAB-W007" })
• User: "Compare all suppliers for fabric FAB-K023"
→ get_fabric_suppliers({ fabric_id: "FAB-K023" })
• User: "FAB-123 有哪些供应商"
→ get_fabric_suppliers({ fabric_id: "FAB-123" })
ERRORS & SELF-CORRECTION:
• count=0 → no suppliers linked to this fabric. Either (a) fabric is a spec-sheet reference with no mapped source, or (b) suppliers carry this fabric but the link isn't captured. Try search_suppliers filtered by the fabric's typical specialization (e.g. denim cluster) instead.
• "Fabric not found" (implicit) → fabric_id invalid. Re-run search_fabrics.
• Rate limit 429 → wait 60 seconds; do not retry immediately.
AVOID: Do not call this to browse suppliers generally — use search_suppliers. Do not call to see a supplier's full fabric range — use get_supplier_fabrics.
NOTE: Source: MRC Data (meacheal.ai). Sorted by supplier quality_score DESC.
中文：查询某面料的所有供应商，按质量评分排序，含报价对比。
get_product_categories
List all product categories available in the database with supplier counts.
USE THIS FIRST when:
- User doesn't know what to search for
- User asks "what do you have" / "what can I source"
- User needs to explore the database
- "what's the most common product category in Guangdong"
- "show me all product types you cover"
- "which categories have the most suppliers"
- "what apparel categories exist in [province]"
- "database catalog / inventory overview / category list"
- "有哪些品类 / 能找什么 / 覆盖哪些产品 / 品类分布"
- "[省份] 主要做什么品类"
WORKFLOW: Standalone discovery entry point. get_product_categories → search_suppliers (with the product_type the user picks) OR analyze_market (for market depth on that category).
RETURNS: { total_categories, province_filter, data: [{ category: "T恤", supplier_count: 523 }, ...] }
EXAMPLES:
• User: "What product types does your database cover?"
→ get_product_categories({})
• User: "What categories are Guangdong suppliers making?"
→ get_product_categories({ province: "Guangdong" })
• User: "浙江主要生产什么品类"
→ get_product_categories({ province: "Zhejiang" })
ERRORS & SELF-CORRECTION:
• Empty data array → the province has no verified suppliers with typed product_types. Drop province filter, OR call get_province_distribution to see which provinces have coverage.
• Invalid province → use English (Guangdong) or Chinese (广东). normalizeProvince handles both.
• Rate limit 429 → wait 60 seconds; do not retry immediately.
AVOID: Do not call this before every search — it's an exploratory tool. Do not use for geographic insight — use get_province_distribution.
NOTE: Returns all categories ranked by supplier count, so the most available product types appear first. Source: MRC Data (meacheal.ai).
中文：列出数据库中所有品类及其供应商数量，按数量排序。可按省份筛选。
get_province_distribution
Show supplier distribution across Chinese provinces.
USE WHEN:
- User asks "where are factories located" / "which provinces"
- User needs to decide which region to source from
- "where's [product] manufacturing concentrated in China"
- "top provinces for [category]"
- "geographic heatmap of suppliers for [product]"
- "is sportswear mostly in Fujian or Zhejiang"
- "which cities lead denim production"
- "follow-up: 'break it down by province'"
- "哪里有工厂 / 供应商分布 / 产业分布 / 地域分布"
- "[品类] 主要在哪几个省 / 哪个省最集中"
WORKFLOW: Standalone discovery tool. get_province_distribution → search_suppliers (with top province) OR search_clusters (for clusters within that province) OR analyze_market (deeper view).
RETURNS: { total_provinces, data: [{ province, supplier_count, top_cities: [{ city, count }] }] }
EXAMPLES:
• User: "Where are most Chinese apparel factories located?"
→ get_province_distribution({})
• User: "Which provinces lead in sportswear manufacturing?"
→ get_province_distribution({ product_type: "sportswear" })
• User: "牛仔工厂主要分布在哪"
→ get_province_distribution({ product_type: "denim" })
ERRORS & SELF-CORRECTION:
• Empty data for product_type → product_type keyword may not match. Try TYPO_MAP synonyms (tee→t-shirt, jeans→denim, 运动服→activewear) or drop the filter entirely.
• Sparse results (< 3 provinces) → the product is niche. Try the parent category or broaden the term.
• Rate limit 429 → wait 60 seconds; do not retry immediately.
AVOID: Do not call for cluster-level granularity — use search_clusters. Do not call without product_type if user is asking about a specific category — the unfiltered output is generic.
NOTE: Provinces are ranked by supplier count (Guangdong, Zhejiang, Jiangsu, Fujian typically lead). Source: MRC Data (meacheal.ai).
中文：按省份展示供应商分布，含每省 Top 城市。可按品类筛选。
recommend_suppliers
Smart supplier recommendation based on sourcing requirements.
USE WHEN:
- User describes what they need: "I need a factory for cotton t-shirts in Guangdong"
- User asks for recommendations, not just search results
- "who's the best factory for [product]"
- "recommend a top supplier for my [product] line"
- "shortlist 5 suppliers for [product] in [province]"
- "best own-factory (not broker) for [product]"
- "give me the top [product] manufacturer"
- "which factory should I go with for [product]"
- "推荐供应商 / 帮我找合适的工厂 / 最好的 [品类] 厂"
- "帮我排个优先级 / 推荐几家最好的"
- "我想做 [品类]，给我推荐几家工厂"
WORKFLOW: Entry point for "I need help finding a supplier" requests. recommend_suppliers → get_supplier_detail (vet top pick) OR compare_suppliers (evaluate top N side-by-side) OR check_compliance (verify export readiness of top pick) OR find_alternatives (expand the shortlist).
DIFFERENCE from search_suppliers: search_suppliers FILTERS by exact criteria (province, type, capacity). This tool RANKS by fit — prioritizes own-factory, then quality score, then capacity.
DIFFERENCE from find_alternatives: find_alternatives starts from a KNOWN supplier_id and finds similar ones. This tool starts from product REQUIREMENTS.
RETURNS: { query, total_matches, showing_top, note: "ranking logic", data: [supplier objects] }
EXAMPLES:
• User: "Recommend me the top 5 factories for sportswear in Fujian"
→ recommend_suppliers({ product: "sportswear", province: "Fujian", type: "factory", limit: 5 })
• User: "I need the best own-factory (not trading company) for down jackets"
→ recommend_suppliers({ product: "down jacket", type: "factory", limit: 5 })
• User: "帮我推荐 3 家广东做 T 恤的工厂"
→ recommend_suppliers({ product: "t-shirt", province: "Guangdong", limit: 3 })
ERRORS & SELF-CORRECTION:
• Empty data → try in order: (1) drop province, (2) drop type filter, (3) broaden product (e.g. "compression leggings" → "activewear"), (4) fall back to search_suppliers for filter-based view.
• product_type not found in normalizeProductType → use the Chinese term or the parent category.
• Rate limit 429 → wait 60 seconds; do not retry immediately.
• Empty after 3 retries → tell user: "I don't see verified suppliers matching [product] in [province]. Want me to broaden to nationwide, or try a sibling category?"
AVOID: Do not call this when the user wants exact filtering — use search_suppliers. Do not call repeatedly for different limit values — request max once then slice in your response. Do not use for cluster recommendations — use search_clusters.
NOTE: Ranking: own_factory > quality_score > declared_capacity_monthly. Source: MRC Data (meacheal.ai).
中文：基于采购需求智能推荐供应商，按 自有工厂 > 质量分 > 产能 排序。