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24mlight

A Share MCP

by 24mlight

get_last_n_trading_days

Retrieve recent trading dates for A-share market analysis by specifying the number of days needed for financial calculations and reporting.

Instructions

Return the last N trading dates.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
daysNo

Implementation Reference

  • MCP tool handler function for 'get_last_n_trading_days'. It wraps the core use case logic with standardized error handling via run_tool_with_handling.
    @app.tool()
    def get_last_n_trading_days(days: int = 5) -> str:
        """Return the last N trading dates."""
        return run_tool_with_handling(
            lambda: uc_date.get_last_n_trading_days(active_data_source, days=days),
            context=f"get_last_n_trading_days:{days}",
        )
  • Core implementation that fetches recent trade dates from the financial data source and returns the last N trading days as a comma-separated string.
    def get_last_n_trading_days(data_source: FinancialDataSource, *, days: int) -> str:
        today = datetime.now()
        start = (today - timedelta(days=days * 2)).strftime("%Y-%m-%d")
        end = today.strftime("%Y-%m-%d")
        df = _fetch_trading_days(data_source, start_date=start, end_date=end)
        trading_days = df[df["is_trading_day"] == "1"]["calendar_date"].tolist()
        return ", ".join(trading_days[-days:]) if trading_days else ""
  • mcp_server.py:56-56 (registration)
    Invocation of the registration function that adds the get_last_n_trading_days tool (among others) to the FastMCP app.
    register_date_utils_tools(app, active_data_source)
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 of behavioral disclosure. It states the tool returns dates but doesn't explain what constitutes a 'trading day' (e.g., excludes weekends/holidays), the format of the return (e.g., list of strings, timestamps), or any constraints like rate limits or data freshness. This leaves significant gaps for a tool with no annotation coverage.

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 no wasted words. It's front-loaded with the core purpose, making it easy to parse quickly. Every part of the sentence contributes directly to understanding the tool's function.

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 the lack of annotations and output schema, the description is incomplete for a tool that returns data. It doesn't specify the return format (e.g., list, JSON structure), what 'trading days' means contextually, or error handling. For a data-fetching tool with no structured output documentation, this leaves too many unknowns for reliable agent use.

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

Parameters3/5

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

The schema description coverage is 0%, so the description must compensate. It mentions 'last N trading dates' which implies the 'days' parameter, but doesn't clarify semantics like whether N includes today, the range of valid values, or what happens with negative/zero inputs. This adds minimal value beyond the schema's basic structure, warranting a baseline score.

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 tool's purpose with a specific verb ('Return') and resource ('last N trading dates'), making it easy to understand what the tool does. However, it doesn't explicitly differentiate from similar siblings like 'get_trade_dates' or 'get_latest_trading_date', which could cause confusion about when to use each.

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 siblings like 'get_trade_dates' and 'get_latest_trading_date' that might overlap in functionality, the lack of explicit usage context or exclusions leaves the agent without clear direction for tool selection.

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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