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ariesanhthu

VNStock MCP Server

by ariesanhthu
OverviewSchemaRelated ServersScoreDiscussions
Python
Hybrid

get_fund_top_holding

Retrieve the top holdings of a specific fund in the Vietnam stock market. Provide the fund symbol to get detailed holding information in JSON or DataFrame format.

Instructions

Get top holding of a fund from stock market
Args:
    symbol: str (symbol of the fund to get top holding)
    output_format: Literal['json', 'dataframe'] = 'json'
Returns:
    pd.DataFrame

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
symbolYes
output_formatNojson

Implementation Reference

  • src/vnstock_mcp/server.py:558-576 (handler)
    The main handler function for the 'get_fund_top_holding' MCP tool. It is registered via @server.tool() decorator, defines input schema via type hints, fetches top holdings using vnstock FMarketFund.details.top_holding(), and returns data as JSON or pandas DataFrame.
    @server.tool()
def get_fund_top_holding(
    symbol: str, output_format: Literal["json", "dataframe"] = "json"
):
    """
    Get top holding of a fund from stock market
    Args:
        symbol: str (symbol of the fund to get top holding)
        output_format: Literal['json', 'dataframe'] = 'json'
    Returns:
        pd.DataFrame
    """
    fund = FMarketFund()
    df = fund.details.top_holding(symbol=symbol)
    if output_format == "json":
        return df.to_json(orient="records", force_ascii=False)
    else:
        return df
  • src/vnstock_mcp/server.py:18-18 (registration)
    Creation of the FastMCP server instance where all tools including get_fund_top_holding are registered via decorators.
    server = FastMCP("VNStock MCP Server")
  • src/vnstock_mcp/server.py:6-6 (helper)
    Import of FMarketFund class used in the tool handler to fetch fund details.
    from vnstock.explorer.fmarket.fund import Fund as FMarketFund

Tool Definition Quality

C2.9/5.0
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. It mentions the return type (pd.DataFrame) and output format options, but lacks critical behavioral details such as data source, rate limits, authentication needs, error handling, or whether it's a read-only operation. For a tool with no annotations, this leaves significant gaps in understanding its behavior.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness3/5

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

The description is front-loaded with the purpose, followed by parameter and return details in a structured format. However, the 'Returns: pd.DataFrame' contradicts the output_format options (json/dataframe), creating confusion. The structure is efficient but marred by this inconsistency.

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 0% schema description coverage, the description is incomplete. It covers basic purpose and parameters but misses behavioral context, error handling, and clear return value explanation. For a tool with two parameters and financial data, more detail is needed to ensure proper use.

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

Parameters4/5

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 adds meaning by specifying that 'symbol' is the fund symbol and 'output_format' controls the return format with default 'json', which clarifies beyond the bare schema. However, it doesn't detail format specifics or constraints, leaving some ambiguity.

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 top holding of a fund from stock market' with a specific verb ('Get') and resource ('top holding of a fund'), distinguishing it from sibling tools like get_fund_asset_holding or get_fund_industry_holding. However, it doesn't explicitly differentiate from get_fund_nav_report or list_all_funds, which could be related but serve different purposes.

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 sibling tools like get_fund_asset_holding or get_fund_industry_holding, nor does it specify prerequisites or context for usage. The agent must infer usage from the purpose 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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