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ariesanhthu

VNStock MCP Server

by ariesanhthu

get_company_shareholders

Retrieve shareholder information for Vietnamese companies by stock symbol to analyze ownership structures and investment decisions.

Instructions

Get company shareholders 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 implements the get_company_shareholders tool. It creates a TCBSCompany instance, calls its shareholders() method to fetch data, and returns it as JSON or pandas DataFrame based on output_format. The @server.tool() decorator handles both registration and schema inference from type hints.
    @server.tool()
    def get_company_shareholders(
        symbol: str, output_format: Literal["json", "dataframe"] = "json"
    ):
        """
        Get company shareholders from stock market
        Args:
            symbol: str
            output_format: Literal['json', 'dataframe'] = 'json'
        Returns:
            pd.DataFrame
        """
        equity = TCBSCompany(symbol=symbol)
        df = equity.shareholders()
        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 lacks critical details like whether this is a read-only operation, potential rate limits, authentication needs, or what happens with invalid symbols. The description is minimal and misses key behavioral traits.

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 brief and structured with clear sections for Args and Returns, making it easy to parse. It avoids unnecessary fluff, though the title 'null' is redundant and could be omitted for better efficiency.

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 no annotations, no output schema, and low schema coverage, the description is incomplete. It covers basic parameters and return type but omits error handling, data freshness, source details, and how results are structured (e.g., columns in the DataFrame). For a financial data tool, this leaves significant gaps in understanding.

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 both parameters (symbol and output_format) and explains output_format's options and default, adding value beyond the schema. However, it doesn't clarify what 'symbol' represents (e.g., stock ticker format) or provide examples, 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 shareholders from stock market', making the purpose specific and understandable. It distinguishes itself from siblings like get_company_officers or get_company_subsidiaries by focusing on shareholders, though it doesn't explicitly contrast with them.

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. While the description implies it's for retrieving shareholder data, it doesn't specify prerequisites, limitations, or when other tools like get_company_overview might be more appropriate, leaving usage context 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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