mcp-knowledge

china_market_data

Read-only

Chinese capital market intelligence for the ZH diaspora (50M+) and institutional investors. Covers A-Shares (SSE/SZSE), H-Shares (HKEX), and ADRs across four modes:

• company — full company profile: name ZH/EN, USCC (18-digit social credit code), exchange, industry (CSRC classification), chairperson, registered capital, SOE flag • market_quote — real-time quote: price (CNY or HKD), change%, volume, market cap, P/E ratio, dividend yield, last update timestamp • sector_overview — sector snapshot: top 5 companies by market cap, avg P/E, 30-day sector index change. Supported sectors: semiconductor, ev, battery, technology, finance, energy, realestate, consumer, pharma, telecom • regulatory_filing — recent regulatory disclosures (HKEX filings: annual, quarterly, announcements, mergers, IPOs) with title, date, document URL

Input formats accepted: • 6-digit A-Share ticker (e.g. '600519' for Moutai SSE) • HKEX ticker (e.g. '0700.HK' or '700' for Tencent) • Company name in EN or ZH (e.g. '腾讯', 'Kweichow Moutai') • Sector keyword (e.g. 'semiconductor', '半导体')

Data sources: Yahoo Finance (primary, always accessible), Eastmoney push2 + CompanySurvey (via Bright Data proxy when AICI_RESEARCH_PROXY_URL is set), HKEX filing API. Note: Eastmoney/CSRC/SSE are blocked from datacenter IPs without proxy — set AICI_RESEARCH_PROXY_URL to unlock full coverage.

Input Schema

TableJSON Schema

Name	Required	Description
`mode`	Yes	Analysis mode. company=full profile, market_quote=price data, sector_overview=top 5 by sector, regulatory_filing=recent filings.
`async`	No	If 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.
`query`	Yes	Ticker (6-digit A-share, 4-digit HK, Yahoo format), company name (ZH or EN), or sector keyword.
`exchange`	No	Exchange filter. Default: all. Affects sector_overview ticker selection.
`period_days`	No	Lookback period in days for regulatory filings. Default: 30.

Output Schema

TableJSON Schema

Name	Required	Description	Default
`mode`	Yes
`query`	Yes
`status`	Yes
`company`	No
`sources`	Yes
`market_quote`	No
`quality_score`	Yes
`sector_overview`	No
`regulatory_filings`	No

TDQS

A4.2/5.0

Behavior4/5

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

Annotations indicate readOnly and not destructive, and the description adds valuable behavioral context: it discloses data sources (Yahoo Finance, Eastmoney, HKEX), notes that some sources require a proxy (AICI_RESEARCH_PROXY_URL), and implies potential latency for proxy-dependent modes. This goes beyond 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 bullet points and clear sections, front-loading the main purpose. It is slightly verbose but each section contributes meaningful detail, balancing completeness with readability.

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 (4 modes, 5 parameters, multiple data sources, proxy requirement, async option), the description is thorough. It covers all input formats, mode details, data source caveats, and references related tools (job_result). The presence of an output schema further reduces the need to describe return values.

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?

The input schema covers all 5 parameters with descriptions (100% coverage), so the description adds limited semantic value. It provides examples (e.g., '600519' for Moutai) but does not significantly enhance understanding beyond the schema.

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 identifies the tool as providing Chinese capital market intelligence, listing four specific modes (company, market_quote, sector_overview, regulatory_filing) and covering A-Shares, H-Shares, and ADRs. It distinguishes itself from sibling tools like china_ecommerce_intel and india_market_data by its focused domain.

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 details the four modes and accepted input formats, providing clear context for when to use each mode. However, it does not explicitly state when not to use the tool or compare it with alternatives within the sibling set.

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.

china_market_data

Input Schema

Output Schema

TDQS

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TDQS

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