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MaoBui2907

VNStock MCP Server

by MaoBui2907

get_quote_history_price

Retrieve historical stock price quotes for a given symbol and date range, with customizable intervals and output formats optimized for AI analysis.

Instructions

Get quote price history of a symbol from stock market Args: symbol: str (symbol to get history price) start_date: str (format: YYYY-MM-DD) end_date: str = None (end date to get history price. None means today) interval: Literal['1m', '5m', '15m', '30m', '1H', '1D', '1W', '1M'] = '1D' (interval to get history price) output_format: Literal['json', 'dataframe', 'toon'] = 'toon' (output format, 'toon' is optimized for AI) Returns: pd.DataFrame

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
symbolYes
start_dateYes
end_dateNo
intervalNo1D
drop_market_closeNo
output_formatNotoon
Behavior2/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations provided, so the description carries full burden. It lists parameters but does not disclose behavioral traits such as data freshness, rate limits, authentication requirements, or error behavior. The output format is mentioned but not fully explained.

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 fairly concise with a clear first sentence and an Args block. It could be more structured (e.g., separate behaviour from parameters) but overall efficient.

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 no output schema or annotations, the description explains parameters and returns but lacks detail on output structure, error handling, or limitations. For a historical data tool, coverage is adequate but not thorough.

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 adds meaning for most parameters (symbol, start_date, end_date, interval, output_format) with formats and defaults. However, it omits the 'drop_market_close' parameter entirely, which is in the schema.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool retrieves quote price history for a stock market symbol. It uses specific verbs and resources ('Get quote price history') and distinguishes from siblings like get_quote_intraday_price which focuses on intraday 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?

No explicit guidance on when to use this tool versus alternatives like get_quote_intraday_price or get_quote_price_with_indicators. The description lists interval options but does not specify context 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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