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a-share-mcp

a-share-mcp

by shouldnotappearcalm
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Remote

get_historical_k_data

Fetch historical OHLCV K-line data for Chinese A-share stocks by code and date range, with configurable frequency, price adjustment, field selection, row limit, and markdown/JSON/CSV output.

Instructions

    Fetches historical K-line (OHLCV) data for a Chinese A-share stock.

    Args:
        code: The stock code in Baostock format (e.g., 'sh.600000', 'sz.000001').
        start_date: Start date in 'YYYY-MM-DD' format.
        end_date: End date in 'YYYY-MM-DD' format.
        frequency: Data frequency. Valid options (from Baostock):
                     'd': daily
                     'w': weekly
                     'm': monthly
                     '5': 5 minutes
                     '15': 15 minutes
                     '30': 30 minutes
                     '60': 60 minutes
                   Defaults to 'd'.
        adjust_flag: Adjustment flag for price/volume. Valid options (from Baostock):
                       '1': Forward adjusted (后复权)
                       '2': Backward adjusted (前复权)
                       '3': Non-adjusted (不复权)
                     Defaults to '3'.
        fields: Optional list of specific data fields to retrieve (must be valid Baostock fields).
                If None or empty, default fields will be used (e.g., date, code, open, high, low, close, volume, amount, pctChg).
        limit: Max rows to return. Defaults to 250.
        format: Output format: 'markdown' | 'json' | 'csv'. Defaults to 'markdown'.

        Returns:
            A Markdown formatted string containing the K-line data table, or an error message.
            The table might be truncated if the result set is too large.

Input Schema

NameRequiredDescriptionDefault
codeYes
limitNo
fieldsNo
formatNomarkdown
end_dateYes
frequencyNod
start_dateYes
adjust_flagNo3

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A3.8/5.0
Behavior3/5

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

With no annotations, the description carries the full burden. It discloses behavioral traits such as data source ('Baostock'), output format options, default values, and potential truncation for large result sets. However, it lacks details on error handling, rate limits, authentication needs, or data freshness, which are important for a data-fetching tool.

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 clear sections (Args, Returns) and uses bullet-like formatting for parameters. It's appropriately sized but could be more front-loaded; the core purpose is stated first, but some details are verbose. Every sentence adds value, though minor trimming is possible.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the complexity (8 parameters, no annotations, no output schema), the description is largely complete. It covers parameters thoroughly and explains the return format. However, it lacks information on error cases, data limits beyond 'truncated', and integration with sibling tools, leaving minor gaps for full contextual understanding.

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?

The description adds significant meaning beyond the input schema, which has 0% schema description coverage. It explains each parameter's purpose, valid options (e.g., frequency and adjust_flag enums), defaults, and examples (e.g., code format 'sh.600000'), fully compensating for the schema's lack of documentation.

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 verb ('fetches') and resource ('historical K-line (OHLCV) data for a Chinese A-share stock'), making the purpose specific and actionable. It distinguishes from siblings by focusing on OHLCV data rather than other financial metrics like balance sheets or dividends.

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

Usage Guidelines2/5

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

No explicit guidance on when to use this tool versus alternatives is provided. While the purpose implies it's for OHLCV data, it doesn't mention when to choose this over other data-fetching tools or prerequisites like data availability. The description lacks context for tool selection among siblings.

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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