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24mlight

A-Share MCP Server

get_money_supply_data_month

Retrieve monthly money supply data for China's A-share market analysis. Specify date ranges to access macroeconomic indicators for financial research.

Instructions

Monthly money supply data.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
start_dateNo
end_dateNo
limitNo
formatNomarkdown

Implementation Reference

  • The primary handler function for the 'get_money_supply_data_month' tool. Decorated with @app.tool(), it captures parameters and delegates execution to the use case via run_tool_with_handling for consistent error handling, logging, and execution.
    @app.tool()
    def get_money_supply_data_month(start_date: Optional[str] = None, end_date: Optional[str] = None, limit: int = 250, format: str = "markdown") -> str:
        """Monthly money supply data."""
        return run_tool_with_handling(
            lambda: fetch_money_supply_data_month(
                active_data_source, start_date=start_date, end_date=end_date, limit=limit, format=format
            ),
            context="get_money_supply_data_month",
        )
  • Core helper function fetch_money_supply_data_month that performs data retrieval from the FinancialDataSource interface using get_money_supply_data_month, validates output format, and formats the resulting DataFrame as a markdown table.
    def fetch_money_supply_data_month(data_source: FinancialDataSource, *, start_date: Optional[str], end_date: Optional[str], limit: int, format: str) -> str:
        validate_output_format(format)
        df = data_source.get_money_supply_data_month(start_date=start_date, end_date=end_date)
        meta = {"dataset": "money_supply_month", "start_date": start_date, "end_date": end_date}
        return format_table_output(df, format=format, max_rows=limit, meta=meta)
  • mcp_server.py:55-55 (registration)
    Registration point where register_macroeconomic_tools is called on the FastMCP app instance, which internally defines and registers the get_money_supply_data_month tool along with other macroeconomic tools.
    register_macroeconomic_tools(app, active_data_source)
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/write operation, authentication needs, rate limits, data freshness, or output format. It lacks any behavioral traits beyond the minimal resource mention.

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 extremely concise with a single phrase, which is appropriately sized for a simple tool. However, it's under-specified rather than efficiently informative—it lacks front-loaded critical details like the action verb. No wasted words, but misses essential content.

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

Completeness2/5

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

Given 4 parameters with 0% schema coverage, no annotations, and no output schema, the description is incomplete. It doesn't explain what the tool returns, how to interpret results, or parameter usage. For a data retrieval tool with multiple inputs, more context is needed to be usable.

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

Parameters2/5

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

Schema description coverage is 0%, so the description must compensate by explaining parameters. It adds no meaning beyond the schema—doesn't clarify what 'start_date', 'end_date', 'limit', or 'format' do, their expected formats (e.g., YYYY-MM-DD), or default behaviors. With 4 parameters and no schema descriptions, this is inadequate.

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

Purpose3/5

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

The description 'Monthly money supply data' states the resource (money supply data) and temporal granularity (monthly), but lacks a specific verb. It distinguishes from sibling 'get_money_supply_data_year' by specifying monthly vs yearly data, but doesn't clarify what action is performed (e.g., retrieve, list, fetch). The purpose is somewhat vague without an explicit action.

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 guidance is provided on when to use this tool versus alternatives. It doesn't mention sibling tools like 'get_money_supply_data_year' for yearly data or other financial data tools in the list. There's no context about prerequisites, typical use cases, or exclusions.

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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