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24mlight

A Share MCP

get_growth_data

Retrieve quarterly growth capability data for A-share stocks by specifying stock code, year, and quarter to analyze financial performance trends.

Instructions

Quarterly growth capability data.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
codeYes
yearYes
quarterYes
limitNo
formatNomarkdown

Implementation Reference

  • MCP tool handler for 'get_growth_data', decorated with @app.tool(). Accepts code, year, quarter, limit, and format parameters. Delegates execution to the fetch_growth_data use case wrapped in run_tool_with_handling for error handling and caching.
    @app.tool()
    def get_growth_data(code: str, year: str, quarter: int, limit: int = 250, format: str = "markdown") -> str:
        """Quarterly growth capability data."""
        return run_tool_with_handling(
            lambda: fetch_growth_data(active_data_source, code=code, year=year, quarter=quarter, limit=limit, format=format),
            context=f"get_growth_data:{code}:{year}Q{quarter}",
        )
  • mcp_server.py:52-52 (registration)
    Registration of financial report tools, including 'get_growth_data', by calling the register_financial_report_tools function with the MCP app and active data source.
    register_financial_report_tools(app, active_data_source)
  • Helper use case function that performs input validation, fetches raw growth data from the FinancialDataSource interface, and formats the output as a table in markdown or other specified format.
    def fetch_growth_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_growth_data(code=code, year=year, quarter=quarter)
        return _format_financial_df(df, code=code, year=year, quarter=quarter, dataset="Growth", format=format, limit=limit)
  • Registration function that defines and registers all financial report tools, including the get_growth_data handler using @app.tool() decorator.
    def register_financial_report_tools(app: FastMCP, active_data_source: FinancialDataSource):
        """
        Register financial report related tools with the MCP app.
        """
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 reveals nothing about whether this is a read-only operation, whether it requires authentication, what format the data returns in, whether there are rate limits, or what happens when parameters are invalid. For a tool with 5 parameters and no annotation coverage, this is completely inadequate.

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

Conciseness2/5

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

While technically concise with just 3 words, this is a case of harmful under-specification rather than effective brevity. The description fails to provide the basic information needed to understand or use the tool, making it inefficient for an AI agent that needs to quickly comprehend tool purposes.

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?

Given the complexity (5 parameters, 3 required), complete lack of annotations, no output schema, and 0% schema description coverage, the description is completely inadequate. It provides no meaningful context about what the tool does, how to use it, what it returns, or how it differs from the many other financial data tools in the server.

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 (code, year, quarter, limit, format), the description provides absolutely no information about what these parameters mean or how they should be used. The description doesn't mention any parameters at all, leaving the agent to guess what 'code' refers to (stock code? industry code?), what format 'format' accepts beyond the default 'markdown', or what 'growth capability data' actually contains.

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 growth capability data' is vague and tautological - it essentially restates the tool name 'get_growth_data' without specifying what action the tool performs. It doesn't clearly state whether this retrieves, calculates, analyzes, or generates growth data, nor does it distinguish this from sibling tools like get_profit_data or get_operation_data that also appear to fetch financial metrics.

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?

The description provides no guidance on when to use this tool versus alternatives. With numerous sibling tools fetching various financial data types (balance_data, cash_flow_data, dividend_data, etc.), there's no indication of what makes 'growth capability data' distinct or when an agent should choose this specific tool over others in the server.

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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