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ariesanhthu

VNStock MCP Server

by ariesanhthu

get_raw_report

Retrieve raw financial reports for Vietnamese companies by stock symbol, with options for quarterly or annual periods and JSON or dataframe output formats.

Instructions

Get raw report of a company from stock market
Args:
    symbol: str (symbol of the company to get raw report)
    period: Literal['quarter', 'year'] = 'year' (period to get raw report)
    output_format: Literal['json', 'dataframe'] = 'json'
Returns:
    pd.DataFrame

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
symbolYes
periodNoyear
output_formatNojson

Implementation Reference

  • The main handler function for the 'get_raw_report' MCP tool. It is decorated with @server.tool(), which registers it as an MCP tool and infers the schema from type annotations and docstring. The function fetches raw financial reports using VCIFinance from the vnstock library and returns data in JSON or DataFrame format.
    @server.tool()
    def get_raw_report(
        symbol: str,
        period: Literal["quarter", "year"] = "year",
        output_format: Literal["json", "dataframe"] = "json",
    ):  # pyright: ignore[reportUndefinedVariable]
        """
        Get raw report of a company from stock market
        Args:
            symbol: str (symbol of the company to get raw report)
            period: Literal['quarter', 'year'] = 'year' (period to get raw report)
            output_format: Literal['json', 'dataframe'] = 'json'
        Returns:
            pd.DataFrame
        """
        finance = VCIFinance(symbol=symbol, period=period)
        df = finance._get_report(mode="raw")
        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?

No annotations are provided, so the description carries full burden. It mentions the return type (pd.DataFrame) but lacks critical behavioral details: what constitutes a 'raw report' (e.g., financial statements, metrics, unstructured data), whether it requires authentication, rate limits, data freshness, or error handling. The description is insufficient for a mutation-free read operation with 3 parameters.

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 appropriately sized but not optimally structured. It front-loads the purpose but uses a code-like format (Args/Returns) that might be less readable. Every sentence earns its place, but the formatting could be more natural for an AI agent. It's concise but could be clearer in prose.

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 3 parameters, no annotations, and no output schema, the description is moderately complete. It covers parameters well but lacks behavioral context (e.g., what 'raw report' contains, data sources, limitations). The return type is mentioned (pd.DataFrame), but without an output schema, more detail on the structure would help. It's adequate but has clear gaps for a financial data tool.

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%, but the description compensates well by documenting all 3 parameters in the Args section with types, enums, and defaults. It adds meaning beyond the bare schema by explaining 'symbol' as 'symbol of the company', 'period' as 'period to get raw report' with options, and 'output_format' with options. This covers parameter semantics adequately despite the schema gap.

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 action ('Get raw report') and resource ('of a company from stock market'), providing a specific verb+resource combination. However, it doesn't explicitly differentiate this tool from similar siblings like 'get_company_reports' or 'get_company_overview', which might offer related financial data.

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. With many sibling tools available for company data (e.g., get_balance_sheets, get_income_statements, get_company_overview), there's no indication of what makes 'raw report' distinct or when it should be preferred over other options.

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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