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ariesanhthu

VNStock MCP Server

by ariesanhthu

get_company_trading_stats

Retrieve trading statistics for Vietnam stock market companies by symbol, providing key market data for analysis and decision-making.

Instructions

Get company trading stats from stock market
Args:
    symbol: str
    output_format: Literal['json', 'dataframe'] = 'json'
Returns:
    pd.DataFrame

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
symbolYes
output_formatNojson

Implementation Reference

  • The handler function decorated with @server.tool(), implementing the core logic to fetch company trading stats using vnstock's VCICompany.trading_stats() and format output as JSON or DataFrame.
    @server.tool()
    def get_company_trading_stats(
        symbol: str, output_format: Literal["json", "dataframe"] = "json"
    ):
        """
        Get company trading stats from stock market
        Args:
            symbol: str
            output_format: Literal['json', 'dataframe'] = 'json'
        Returns:
            pd.DataFrame
        """
        equity = VCICompany(symbol=symbol)
        df = equity.trading_stats()
        if output_format == "json":
            return df.to_json(orient="records", force_ascii=False)
        else:
            return df
  • The @server.tool() decorator registers the get_company_trading_stats function as an MCP tool.
    @server.tool()
Behavior2/5

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

No annotations are provided, so the description carries full burden. It mentions the return type 'pd.DataFrame' and output formats, but lacks details on permissions, rate limits, data freshness, or error handling. For a tool fetching trading stats, this is a significant gap in 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.

Conciseness4/5

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

The description is front-loaded with the purpose, followed by structured Args and Returns sections, making it efficient. However, the 'Returns' section could be integrated more smoothly, and some redundancy exists (e.g., stating 'pd.DataFrame' when output_format includes it).

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

Completeness3/5

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

Given no annotations, no output schema, and low schema coverage, the description is moderately complete. It covers basic parameters and return type but misses details on data scope, time ranges, or error cases, which are important for a trading stats tool in a context with many financial data siblings.

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?

Schema description coverage is 0%, but the description adds value by explaining 'symbol' as a string and 'output_format' with its enum values and default. However, it doesn't clarify what 'symbol' represents (e.g., ticker symbol) or the implications of choosing 'json' vs 'dataframe', leaving some semantic gaps.

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 verb 'Get' and the resource 'company trading stats from stock market', making the purpose evident. However, it doesn't differentiate from sibling tools like 'get_company_overview' or 'get_price_board', which might also retrieve stock market data, so it's not fully specific to sibling context.

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. With many sibling tools for company data (e.g., 'get_company_overview', 'get_quote_history_price'), the description lacks context on what makes this tool unique or when it's appropriate, offering only basic usage without exclusions or comparisons.

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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