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24mlight

A Share MCP

get_operation_data

Retrieve quarterly operational capability data for A-share stocks to analyze company performance and make informed investment decisions.

Instructions

Quarterly operation capability data.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
codeYes
yearYes
quarterYes
limitNo
formatNomarkdown

Implementation Reference

  • MCP tool handler for get_operation_data, decorated with @app.tool(). Delegates to use case with error handling and caching.
    @app.tool()
    def get_operation_data(code: str, year: str, quarter: int, limit: int = 250, format: str = "markdown") -> str:
        """Quarterly operation capability data."""
        return run_tool_with_handling(
            lambda: fetch_operation_data(active_data_source, code=code, year=year, quarter=quarter, limit=limit, format=format),
            context=f"get_operation_data:{code}:{year}Q{quarter}",
        )
  • mcp_server.py:52-52 (registration)
    Registration of financial report tools including get_operation_data by calling the register function.
    register_financial_report_tools(app, active_data_source)
  • Use case helper: validates inputs, fetches raw data from data_source, formats and limits output.
    def fetch_operation_data(data_source: FinancialDataSource, *, code: str, year: str, quarter: int, limit: int, format: str) -> str:
        validate_year(year)
        validate_quarter(quarter)
        validate_output_format(format)
        df = data_source.get_operation_data(code=code, year=year, quarter=quarter)
        return _format_financial_df(df, code=code, year=year, quarter=quarter, dataset="Operation Capability", format=format, limit=limit)
  • Core data fetching implementation using Baostock's query_operation_data API.
    def get_operation_data(self, code: str, year: str, quarter: int) -> pd.DataFrame:
        """Fetches quarterly operation capability data using Baostock."""
        return _fetch_financial_data(bs.query_operation_data, "Operation Capability", code, year, quarter)
  • Interface definition specifying the expected signature for data source implementations.
    def get_operation_data(self, code: str, year: str, quarter: int) -> pd.DataFrame:
        pass
Behavior1/5

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

No annotations are provided, so the description carries full burden for behavioral disclosure. The description provides zero information about what the tool actually does behaviorally - whether it's a read operation, what format the data returns in, whether there are rate limits, authentication requirements, or any other behavioral characteristics. It's completely inadequate for a tool with 5 parameters.

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

Conciseness3/5

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

The description is extremely concise - just 4 words - but this is under-specification rather than effective conciseness. While it's technically brief, it fails to provide necessary information, making the brevity detrimental rather than helpful. It's front-loaded only because there's nothing to load.

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

Completeness1/5

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

For a tool with 5 parameters, no annotations, no output schema, and 0% schema description coverage, the description is completely inadequate. It provides no information about what the tool does, how to use it, what the parameters mean, what behavior to expect, or how it differs from similar tools. This leaves an AI agent with insufficient information to properly select or invoke the tool.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters1/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

With 0% schema description coverage and 5 parameters (3 required), the description provides absolutely no information about what the parameters mean or how they should be used. The description doesn't mention any parameters, leaving code, year, quarter, limit, and format completely unexplained despite being essential for proper tool invocation.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose2/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description 'Quarterly operation capability data' is tautological - it essentially restates the tool name 'get_operation_data' without specifying what action is performed. It doesn't clearly state whether this retrieves, calculates, or generates operation data, nor does it distinguish this tool from its many financial data siblings like get_balance_data or get_profit_data.

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

Usage Guidelines1/5

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

No guidance is provided about when to use this tool versus alternatives. With 40+ sibling tools on the server including various financial data retrieval tools, the description offers no indication of what makes operation capability data unique or when an agent should choose this over similar tools like get_profit_data or get_fina_indicator.

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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