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MaoBui2907

VNStock MCP Server

by MaoBui2907

get_cash_flows

Retrieve cash flow statements for a Vietnamese company by stock symbol, with options for quarterly or annual periods and multiple output formats.

Instructions

Get cash flows of a company from stock market Args: symbol: str (symbol of the company to get cash flows) period: Literal['quarter', 'year'] = 'year' (period to get cash flows) output_format: Literal['json', 'dataframe', 'toon'] = 'toon' (output format, 'toon' is optimized for AI) Returns: pd.DataFrame

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
symbolYes
periodNoyear
output_formatNotoon
Behavior2/5

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

With no annotations provided, the description carries full burden. It lists parameters and a return type but does not disclose behavioral aspects such as error conditions, rate limits, data freshness, or side effects. The 'toon' format is mentioned as optimized for AI but not explained further.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a compact docstring with five lines, each serving a purpose: tool purpose, parameter definitions, and return type. There is no extraneous information, and the structure is front-loaded with the main action.

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?

While parameters are well explained, the description lacks context about output behavior (e.g., what 'toon' format returns, how results differ per output_format), error handling, or prerequisites. Given the complexity from sibling tools, some guidance on when to use this over other financial statement tools would improve 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 each parameter's meaning (symbol, period, output_format) and adding a helpful note about 'toon' output. All three parameters are covered with clear semantics.

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 cash flows of a company, using a specific verb ('Get') and resource ('cash flows'). It distinguishes from sibling tools like get_balance_sheets and get_income_statements by focusing on a distinct financial statement.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description implies usage for cash flow data, but does not explicitly guide when to use this tool versus alternatives (e.g., get_income_statements). No when-not-to-use or contextual advice is provided.

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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