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mcp-knowledge

by io.github.getgapup

china_ecommerce_intel

Read-only

Chinese e-commerce intelligence for the ZH diaspora (50M+), import-export teams, brand IP enforcement, MENA/Africa entrepreneurs sourcing from China, and brand monitoring. Covers Taobao, Tmall, JD.com, Pinduoduo, 1688.com (B2B) and AliExpress (cross-border).

Five modes: • product_search — search products by keyword across CN platforms. Returns title ZH/EN, price CNY + USD estimate, sales 30d, rating, seller info, product URL. • seller_profile — full seller/supplier dossier: factory vs reseller detection, certifications (ISO, BSCI, CE), rating, years in business, main categories. • price_history — 12-month price trend for a product (live current price + seasonal model for CN shopping festivals: 11.11, 6.18, CNY). • brand_monitoring — detect counterfeits and grey market listings: price anomaly detection (>50% below MSRP = suspicious), counterfeit keyword scan, risk score 0-100. • market_intel — category overview: top 5 sellers by market share, avg/median price, volume estimate, price range.

Data quality note: LIVE data from Taobao/Tmall/JD/Pinduoduo REQUIRES AICI_RESEARCH_PROXY_URL with CN residential routing (Bright Data -country-cn). Without proxy: AliExpress (cross-border) + curated category fallback available.

Input formats for seller_profile: 'platform:id' e.g. 'aliexpress:123456', '1688:87654321', 'tmall:apple-store-official'. Input formats for price_history: AliExpress product URL or numeric product ID.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
modeYesAnalysis mode. product_search=find products, seller_profile=supplier dossier, price_history=price trend, brand_monitoring=counterfeit detection, market_intel=category overview.
asyncNoIf true, returns a job_id immediately (<200ms) instead of waiting for the result. Poll the result with job_result(job_id). Use for slow tools to avoid client timeouts.
queryYesKeyword, product name, product_id, seller_id (platform:id), brand name, or category. Accepts Chinese characters (ZH) or English.
regionNoMarket region. CN-domestic=full platform coverage, cross-border=AliExpress+1688 focus. Default: CN-domestic.
platformNoTarget platform. Default: all. Note: taobao/tmall/jd/pinduoduo require CN proxy.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
modeYes
statusYes
signalsYes
sourcesYes
productsNo
market_intelNo
platform_usedYes
price_historyNo
quality_scoreYes
seller_profileNo
brand_monitoringNo

TDQS

A4.7/5.0
Behavior5/5

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

Annotations already indicate readOnlyHint=true (safe read operations). The description adds valuable context beyond annotations: data freshness (live data), proxy requirements for certain platforms, input format specifics, and fallback behavior. No contradictions 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 well-structured with an introductory paragraph, bullet points for each mode, a data quality note, and input format examples. It is front-loaded but slightly verbose; one sentence could be trimmed without losing information.

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 modes, multiple platforms, proxy dependency, input formats), the description covers all essential aspects. It explains outputs per mode, data sources, and fallback scenarios. An output schema exists but is not shown; the description compensates by detailing return values.

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?

With 100% schema description coverage, the description still adds significant meaning: it explains what queries can be (keyword, product ID, seller ID, etc.), provides examples for seller_profile and price_history inputs, and elaborates on region and platform choices. This goes beyond the schema's enums and descriptions.

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 provides 'Chinese e-commerce intelligence' for specific audiences and lists five distinct modes (product_search, seller_profile, etc.) with explicit purposes. This distinguishes it from sibling tools, all of which are non-China-e-commerce focused.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description specifies target users (ZH diaspora, import-export teams, etc.) and explains when to use each mode. It also highlights data quality requirements (CN proxy needed for full data). However, it does not explicitly contrast with alternatives or state when not to use the tool.

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

C2.8/5.0
Disambiguation2/5

Many tools have overlapping purposes, especially in competitive intelligence, ESG, and risk assessment. For example, there are multiple tools for competitor analysis (competitive_deep_dive, competitor_intel, competitor_moves, etc.) with unclear boundaries. Agents would struggle to select the correct tool without deep understanding of subtle differences.

Naming Consistency2/5

Tool names are a mix of English and French, and follow no consistent pattern. Some use snake_case (e.g., abm_architect, action_plan_esg), while others are verb-focused (e.g., content_catalog, fx_rate). The lack of a uniform naming convention makes it hard for agents to predict tool names.

Tool Count1/5

With 271 tools, the server is excessively large. Even for a broad knowledge domain, this number of tools makes discovery and selection inefficient. Typical coherent servers have 3-15 tools; this has an order of magnitude more, indicating poor scoping.

Completeness3/5

The tool set covers many domains (compliance, finance, marketing, HR, etc.), but the coverage is uneven due to redundancy. Key areas have multiple overlapping tools, while some sub-domains may still have gaps. Overall, the surface is broad but not well-curated.

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