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ariesanhthu

VNStock MCP Server

by ariesanhthu
OverviewSchemaRelated ServersScoreDiscussions
Python
Hybrid

get_price_board

Retrieve current price board data for specified Vietnam stock symbols, returning results in JSON or DataFrame format for market analysis.

Instructions

Get price board from stock market
Args:
    symbols: list[str] (list of symbols to get price board)
    output_format: Literal['json', 'dataframe'] = 'json'
Returns:
    pd.DataFrame

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
symbolsYes
output_formatNojson

Implementation Reference

  • src/vnstock_mcp/server.py:753-771 (handler)
    The handler function for the 'get_price_board' tool. It is registered via the @server.tool() decorator in FastMCP. Fetches price board data for given symbols using VCITrading and returns as JSON or pandas DataFrame.
    @server.tool()
def get_price_board(
    symbols: list[str], output_format: Literal["json", "dataframe"] = "json"
):
    """
    Get price board from stock market
    Args:
        symbols: list[str] (list of symbols to get price board)
        output_format: Literal['json', 'dataframe'] = 'json'
    Returns:
        pd.DataFrame
    """
    trading = VCITrading()
    df = trading.price_board(symbols_list=symbols)
    if output_format == "json":
        return df.to_json(orient="records", force_ascii=False)
    else:
        return df
  • src/vnstock_mcp/server.py:753-753 (registration)
    The @server.tool() decorator registers the get_price_board function as an MCP tool.
    @server.tool()
  • src/vnstock_mcp/server.py:757-762 (schema)
    Docstring and type hints define the input schema (symbols: list[str], output_format: Literal["json", "dataframe"]) and output (pd.DataFrame or JSON). FastMCP uses this for tool schema.
    """
Get price board from stock market
Args:
    symbols: list[str] (list of symbols to get price board)
    output_format: Literal['json', 'dataframe'] = 'json'
Returns:

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 full burden. It states the tool 'Get's data, implying a read-only operation, but doesn't disclose behavioral traits such as rate limits, authentication needs, data freshness, or error handling. The description is minimal and fails to add meaningful context beyond the basic action.

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 appropriately sized and front-loaded with the core purpose, followed by parameter and return details in a structured format. Every sentence earns its place, though the return statement could be integrated more smoothly. It avoids unnecessary verbosity.

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 description coverage (0%), the description is incomplete. It doesn't explain what a 'price board' returns (e.g., specific fields like price, change, volume) or behavioral aspects like data sources or limitations. For a tool with parameters and financial data retrieval, this leaves significant gaps for an AI agent.

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 'symbols' and 'output_format' with types and a default, adding some semantics beyond the bare schema. However, it doesn't explain what 'price board' includes (e.g., current prices, volumes) or constraints on symbols, leaving gaps in parameter 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 'price board from stock market', which is specific and unambiguous. However, it doesn't differentiate from sibling tools like get_quote_history_price or get_quote_intraday_price, which also retrieve price-related data, so it doesn't fully distinguish its scope from alternatives.

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 like get_quote_history_price or get_quote_intraday_price. It lacks context about what a 'price board' entails compared to other price-related tools, offering no explicit when/when-not instructions or prerequisites.

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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