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ariesanhthu

VNStock MCP Server

by ariesanhthu
OverviewSchemaRelated ServersScoreDiscussions
Python
Hybrid

get_company_officers

Retrieve company officer information from Vietnam's stock market. Filter by current, resigned, or all officers and choose JSON or dataframe output format.

Instructions

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

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
symbolYes
filter_byNoworking
output_formatNojson

Implementation Reference

  • src/vnstock_mcp/server.py:113-133 (handler)
    The handler function implementing the 'get_company_officers' tool. It uses TCBSCompany from vnstock to fetch officers data filtered by status and returns as JSON or DataFrame. Registered via @server.tool() decorator with FastMCP.
    @server.tool()
def get_company_officers(
    symbol: str,
    filter_by: Literal["working", "all", "resigned"] = "working",
    output_format: Literal["json", "dataframe"] = "json",
):  # pyright: ignore[reportUndefinedVariable]  # noqa: E501
    """
    Get company officers from stock market
    Args:
        symbol: str
        filter_by: Literal['working', "all", 'resigned'] = 'working'
        output_format: Literal['json', 'dataframe'] = 'json'
    Returns:
        pd.DataFrame
    """
    equity = TCBSCompany(symbol=symbol)
    df = equity.officers(filter_by=filter_by)
    if output_format == "json":
        return df.to_json(orient="records", force_ascii=False)
    else:
        return df

Tool Definition Quality

C2.9/5.0
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 describe what data the officers contain, whether this is a read-only operation, potential rate limits, authentication needs, or error conditions. For a data retrieval tool with zero annotation coverage, this leaves significant behavioral gaps.

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 efficiently structured with clear sections for Args and Returns. Each sentence serves a purpose: stating the tool's function, documenting parameters, and specifying the return type. While concise, it could be slightly more front-loaded with the core purpose before parameter details.

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 the tool's moderate complexity (3 parameters, no output schema, no annotations), the description is partially complete. It covers the basic purpose and parameters adequately but lacks behavioral context, usage guidance, and details about the returned DataFrame structure. For a financial data tool that likely has specific constraints or data formats, more completeness would be beneficial.

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 description explicitly documents all three parameters with their types and default values, which is valuable since schema description coverage is 0%. It explains what 'filter_by' does (working/all/resigned) and the output_format options. However, it doesn't provide deeper context about what 'symbol' format is expected (ticker symbol, exchange prefix, etc.) or what 'working' versus 'resigned' officers mean in practice.

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: 'Get company officers from stock market' - a specific verb ('Get') and resource ('company officers') with domain context ('stock market'). It distinguishes itself from siblings like get_company_overview or get_company_shareholders by focusing specifically on officers. However, it doesn't explicitly differentiate from potential similar tools not in the sibling list.

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. It doesn't mention prerequisites, when this tool is appropriate compared to other company information tools in the sibling list, or any limitations. The agent must infer usage from the tool name and parameters alone.

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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