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ariesanhthu

VNStock MCP Server

by ariesanhthu
OverviewSchemaRelated ServersScoreDiscussions
Python
Hybrid

get_quote_price_depth

Retrieve detailed price depth data for Vietnam stocks to analyze market liquidity and order book dynamics. Specify a stock symbol and choose JSON or dataframe output format.

Instructions

Get quote price depth from stock market
Args:
    symbol: str (symbol to get price depth)
    output_format: Literal['json', 'dataframe'] = 'json'
Returns:
    pd.DataFrame

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
symbolYes
output_formatNojson

Implementation Reference

  • src/vnstock_mcp/server.py:730-747 (handler)
    The handler function implementing the get_quote_price_depth tool logic, using vnstock.Quote.price_depth(). It is also registered as an MCP tool via the @server.tool() decorator. Supports JSON or DataFrame output.
    @server.tool()
def get_quote_price_depth(
    symbol: str, output_format: Literal["json", "dataframe"] = "json"
):
    """
    Get quote price depth from stock market
    Args:
        symbol: str (symbol to get price depth)
        output_format: Literal['json', 'dataframe'] = 'json'
    Returns:
        pd.DataFrame
    """
    quote = Quote(symbol=symbol, source="VCI")
    df = quote.price_depth()
    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?

No annotations are provided, so the description carries the full burden of behavioral disclosure. It mentions the return type ('pd.DataFrame') but doesn't explain what 'price depth' means (e.g., bid-ask spreads, order book data), whether there are rate limits, authentication requirements, or data freshness. For a financial data tool with zero annotation coverage, this is a significant gap in transparency.

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 well-structured with clear sections for Args and Returns, and it's concise with no wasted words. However, the title is null, and the purpose statement could be more front-loaded with specifics about 'price depth' to improve immediate clarity.

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 lacks details on what 'price depth' returns (e.g., structure of the DataFrame), error handling, data sources, or usage constraints. For a tool with 2 parameters in a financial context, this leaves too much undefined for reliable agent use.

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 with types and a default for 'output_format', adding value beyond the bare schema. However, it doesn't explain what 'symbol' entails (e.g., ticker format, exchange codes) or clarify the difference between 'json' and 'dataframe' outputs, 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 action ('Get quote price depth') and resource ('from stock market'), making the purpose understandable. However, it doesn't differentiate from sibling tools like 'get_quote_history_price' or 'get_quote_intraday_price' which also retrieve price-related data, leaving some ambiguity about what specifically 'price depth' means compared to other quote tools.

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. With multiple sibling tools for quotes (e.g., 'get_quote_history_price', 'get_quote_intraday_price'), the description lacks context on what 'price depth' entails and when it's appropriate compared to other quote retrieval methods, leaving the agent to guess based on tool names 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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