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CNEquity — China A-Share Data Lake for AI Agents

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Python
Hybrid

query_fundamentals

Read-only

Retrieve point-in-time financial statement items as of a required date, preventing look-ahead bias and returning the vintage current then; page results with limit/offset.

Instructions

Financial statement items, point-in-time. as_of is REQUIRED and is what prevents look-ahead: only figures publicly announced on or before that date come back, and a restated figure returns the vintage that was current then. For a present-day view pass today's date; for a historical study pass the date being studied, never today. Results are paged with limit/offset. For prices use query_bars; for valuation ratios read valuation_metrics through query_dataset. Set pit_mode='strict' only to audit what the lake itself held on that date.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
as_ofYesYYYY-MM-DD knowledge cutoff, applied to announce_date.
itemsNoitem_code filter, e.g. ['roe', 'net_profit'].
limitNoMax rows to return (default 200, cap 2000). Responses always report the true total and set `truncated`.
offsetNoRows to skip, for paging with `limit`.
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.
pit_modeNobest_effort (default) also returns rows a later backfill recorded, flagged pit_is_exact=false; strict keeps only rows the lake held by as_of, which is none for a backfilled history.
all_vintagesNoReturn every vintage announced by as_of instead of only the one current then. For studying restatements; it double-counts a fact in any cross-sectional screen.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.17.1

TDQS

A4.9/5.0
Behavior5/5

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

Annotations only declare readOnlyHint=true; the description adds the non-obvious semantics: look-ahead prevention, restated figures returning the then-current vintage, and the warning that all_vintages double-counts a fact in cross-sectional screens. That is exactly the behavioral context annotations cannot carry.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

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

Front-loads the resource and the required parameter, then trades one sentence each for date choice, siblings, paging, and pit_mode. High information density with no filler or restatement of the schema.

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, the description still covers paging/truncation behavior and result semantics, and every parameter is documented either in the schema or here. An agent has everything needed to call it correctly on the first attempt.

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 100%, so the baseline is 3, but the description adds real interpretation: why `as_of` is required and which value to choose, and the audit-only intent behind pit_mode='strict'. The rest (items, limit, offset, symbols) is left to the already-complete 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?

States a specific resource (financial statement items) plus the defining scope: point-in-time with a required `as_of` knowledge cutoff. It explicitly separates itself from query_bars (prices) and query_dataset (valuation ratios), so an agent can pick it without opening other schemas.

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 key decision — pass today's date for a present-day view, the studied date for a historical study, 'never today'. It names the two sibling alternatives by function and restricts pit_mode='strict' to lake-audit use.

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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