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MaoBui2907

VNStock MCP Server

by MaoBui2907

get_price_board

Retrieve price board data for Vietnam stock market symbols. Choose output as JSON, DataFrame, or toon format for AI.

Instructions

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

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
symbolsYes
output_formatNotoon
Behavior3/5

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

With no annotations, the description discloses the return type (pd.DataFrame) and output format options, but lacks information on whether the operation is read-only, what permissions are needed, or data freshness. It does not contradict anything since no annotations exist.

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 relatively concise and structured with 'Args' and 'Returns' sections. However, it repeats the tool's purpose in the first line and then again in the docstring, which is slightly redundant. Still, it front-loads the key functionality.

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, the description provides the return type (pd.DataFrame) but does not detail the columns or how the 'toon' format differs from others. The tool's specificity within many sibling data retrieval tools is unclear, leaving gaps in completeness.

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?

The schema has 0% description coverage, but the description compensates by explaining the purpose of each parameter: symbols are a list of symbols for the price board, and output_format is an enum with 'toon' optimized for AI. This adds meaning beyond the schema types.

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 states a specific verb ('Get') and resource ('price board'), which clearly indicates the tool's function. However, it does not differentiate 'price board' from other price-related siblings like get_quote_history_price or get_quote_intraday_price, leaving ambiguity about what the price board specifically contains.

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. There is no mention of scenarios, prerequisites, or comparisons to sibling tools, leaving the agent without context for selection.

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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