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ariesanhthu

VNStock MCP Server

by ariesanhthu

get_company_subsidiaries

Retrieve subsidiary information for Vietnamese companies listed on the stock market. Use this tool to identify corporate relationships and ownership structures by providing a stock symbol.

Instructions

Get company subsidiaries from stock market
Args:
    symbol: str
    filter_by: Literal["all", "subsidiary"] = "all"
    output_format: Literal['json', 'dataframe'] = 'json'
Returns:
    pd.DataFrame

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
symbolYes
filter_byNoall
output_formatNojson

Implementation Reference

  • The handler function for the 'get_company_subsidiaries' tool. It uses TCBSCompany to fetch subsidiaries data based on symbol and filter, returning it as JSON or DataFrame. The @server.tool() decorator registers it as an MCP tool.
    @server.tool()
    def get_company_subsidiaries(
        symbol: str,
        filter_by: Literal["all", "subsidiary"] = "all",
        output_format: Literal["json", "dataframe"] = "json",
    ):  # pyright: ignore[reportUndefinedVariable]
        """
        Get company subsidiaries from stock market
        Args:
            symbol: str
            filter_by: Literal["all", "subsidiary"] = "all"
            output_format: Literal['json', 'dataframe'] = 'json'
        Returns:
            pd.DataFrame
        """
        equity = TCBSCompany(symbol=symbol)
        df = equity.subsidiaries(filter_by=filter_by)
        if output_format == "json":
            return df.to_json(orient="records", force_ascii=False)
        else:
            return df
Behavior2/5

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It mentions the return type (pd.DataFrame) but doesn't cover critical aspects like data source reliability, rate limits, error handling, or whether it's a read-only operation. The description is minimal and misses key behavioral traits needed for safe invocation.

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 core purpose, followed by parameter and return details in a structured format. It's efficient with minimal waste, though the lack of usage context slightly reduces its effectiveness. Every sentence serves a purpose, but it could be more comprehensive.

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 complexity (3 parameters, 0% schema coverage, no annotations, no output schema), the description is incomplete. It doesn't explain return values beyond 'pd.DataFrame' (e.g., column meanings), error cases, or dependencies. For a tool with financial data retrieval, more context on data freshness and limitations is needed.

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%, so the description must compensate. It lists parameters (symbol, filter_by, output_format) and their types, adding some meaning beyond the schema. However, it doesn't explain what 'symbol' represents (e.g., stock ticker), the difference between 'all' and 'subsidiary' filters, or the implications of output formats, leaving gaps in understanding.

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 resource ('company subsidiaries from stock market'), making the purpose unambiguous. However, it doesn't explicitly differentiate from sibling tools like get_company_overview or get_company_shareholders, which might also retrieve company-related data but for different aspects.

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. The description lacks context such as prerequisites (e.g., valid stock symbols), comparison to siblings (e.g., use this for subsidiary data vs. get_company_overview for general info), or exclusions (e.g., not for historical data).

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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