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CNEquity — China A-Share Data Lake for AI Agents

by rootSunc
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Python
Hybrid

query_bars

Read-only

Fetch daily or intraday China A-share price bars, with hfq/qfq adjustment for returns or current quote levels and universe filtering to exclude untradable names.

Instructions

Daily or intraday price bars. PRICES ARE STORED UNADJUSTED: pass adjust='hfq' for any comparison across time (returns, charts, drawdowns) or a split reads as a crash. Use adjust='qfq' when the levels must match what a quote screen shows today. Pass universe='all_a' for cross-sectional work to drop names that were not tradable that day. datasets: daily_bars, index_bars, minute_bars (1m), minute_bars_5m (5m), and minute_bars_15m / _30m / _60m when the user has derived them (each bar's resampled_from says whether 1m or 5m built it). Every response carries origin: 'lake' is stored and validated; 'live' was fetched just now and cannot be adjusted or universe-filtered — read its warning before using the numbers for anything but a current quote. Not for financial statements (query_fundamentals), other datasets such as fund flow (query_dataset), or ranking and aggregating across many symbols (run_sql).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
endNoInclusive end date, YYYY-MM-DD.
limitNoMax rows to return (default 200, cap 2000). Responses always report the true total and set `truncated`.
startNoInclusive start date, YYYY-MM-DD.
adjustNohfq = back-adjusted, the correct choice for return series. qfq = forward-adjusted to today's price level. total_return (daily_bars only) = hfq with fund cash distributions reinvested; stocks read as hfq, whose factor already reinvests dividends. Omit only when you specifically want raw traded prices for a single day.
offsetNoRows to skip, for paging with `limit`.
datasetNoDefault daily_bars.
symbolsNoSymbols as '<code>.<EX>', e.g. ['600519.SH', '000001.SZ']. Omit for every symbol in range. Use resolve_symbol to turn a company name into a code.
universeNoDrop rows for names unlisted/delisted that day, and ST/suspended ones where trading_status covers it. `all_a` includes SH/SZ/BJ; `all_a_sh_sz` is an explicit SH/SZ-only research subset that excludes BJ. The lake path also requires versioned historical ST evidence; incomplete research fails instead of treating unknown names as normal. daily_bars only.
include_provenanceNoInclude per-row source/data_version/fetched_at. Off by default — the response lists the distinct sources instead, at a fraction of the size.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.17.1

TDQS

A4.8/5.0
Behavior5/5

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

Annotations only declare readOnlyHint=true, so the description carries the real behavioral burden and does: prices are stored UNADJUSTED with a concrete failure mode (a split reads as a crash), the `origin` field distinguishes validated lake data from freshly fetched live data that cannot be adjusted or universe-filtered, and incomplete research fails rather than silently defaulting. This is richer behavioral context than the read-only hint alone.

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?

Dense but front-loaded: the unadjusted-price hazard is stated in the first sentence, before the softer dataset and origin details. Nearly every clause carries information, though the dataset enumeration and resampling parenthetical are slightly overstuffed for a single block of prose.

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?

With no output schema in play, the description compensates by explaining what the response carries (`origin`, its warning, the true total and `truncated` flag) and what the source variants mean. For a 9-parameter analytical tool this is an unusually complete brief.

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?

Schema coverage is already 100%, so the baseline is 3, and the description adds genuine use-case framing on top ('hfq for return series', 'qfq when levels must match a quote screen', 'all_a for cross-sectional work to drop names that were not tradable that day'). It also surfaces the resampled_from and truncation semantics. It stops short of documenting every parameter, but the marginal value is real.

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?

Opens with a specific resource and scope ('Daily or intraday price bars') and explicitly distinguishes itself from siblings by naming query_fundamentals, query_dataset, and run_sql as the wrong tools for adjacent tasks. An agent can route to this tool without opening any schema.

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?

Gives explicit when-to-use rules for the tricky parameters ('adjust=\'hfq\' for any comparison across time', 'adjust=\'qfq\' when levels must match a quote screen', 'universe=\'all_a\' for cross-sectional work') plus a clear exclusion list pointing at three alternative tools. Nothing is left to inference.

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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