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24mlight

A-Share MCP Server

get_industry_members

Retrieve all stocks within a specific industry on a given date from China's A-share market to analyze sector composition and identify constituent companies.

Instructions

Get all stocks in a given industry on a date.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
industryYes
dateNo
limitNo
formatNomarkdown

Implementation Reference

  • MCP tool handler function for 'get_industry_members'. It logs the invocation and delegates execution to the 'fetch_industry_members' use case via 'run_tool_with_handling' for error handling and formatting.
    @app.tool()
    def get_industry_members(industry: str, date: Optional[str] = None, limit: int = 250, format: str = "markdown") -> str:
        """Get all stocks in a given industry on a date."""
        logger.info("Tool 'get_industry_members' called industry=%s, date=%s", industry, date or "latest")
        return run_tool_with_handling(
            lambda: fetch_industry_members(active_data_source, industry=industry, date=date, limit=limit, format=format),
            context=f"get_industry_members:{industry}",
        )
  • Core implementation logic that fetches all stock industry data, filters for the specified industry, handles empty cases, and formats the output as a table.
    def fetch_industry_members(data_source: FinancialDataSource, *, industry: str, date: Optional[str], limit: int, format: str) -> str:
        validate_output_format(format)
        validate_non_empty_str(industry, "industry")
        df = data_source.get_stock_industry(code=None, date=date)
        if df is None or df.empty:
            return "(No data available to display)"
        col = "industry" if "industry" in df.columns else df.columns[-1]
        filtered = df[df[col] == industry].copy()
        meta = {"industry": industry, "as_of": date or "latest"}
        return format_table_output(filtered, format=format, max_rows=limit, meta=meta)
  • mcp_server.py:53-53 (registration)
    Top-level registration call that invokes the registration of index tools, including 'get_industry_members'.
    register_index_tools(app, active_data_source)
  • Function that defines and registers all index-related tools, including the @app.tool() decorator for 'get_industry_members'.
    def register_index_tools(app: FastMCP, active_data_source: FinancialDataSource):
Behavior2/5

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

No annotations are provided, so the description carries the full burden. It mentions retrieving stocks but lacks details on permissions, rate limits, data freshness, or response format. For a tool with 4 parameters and no output schema, this is inadequate behavioral disclosure.

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?

The description is a single, efficient sentence with zero wasted words. It's front-loaded with the core purpose, making it easy to parse quickly, though this brevity contributes to gaps in other dimensions.

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 return values, error handling, or practical usage details, making it insufficient for a tool of this complexity in a data-rich context.

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. It only implies 'industry' and 'date' parameters without explaining their semantics (e.g., format, allowed values). It omits 'limit' and 'format' entirely, failing to add meaningful context beyond the bare schema.

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

Purpose4/5

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

The description clearly states the action ('Get all stocks') and target resource ('in a given industry on a date'), which is specific and unambiguous. However, it doesn't explicitly differentiate from sibling tools like 'get_index_constituents' or 'get_stock_industry', which might have overlapping functionality, preventing a perfect score.

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?

The description provides no guidance on when to use this tool versus alternatives. With many sibling tools related to stocks and industries (e.g., 'get_stock_industry', 'get_index_constituents'), there's no indication of context, prerequisites, or exclusions, leaving usage unclear.

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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