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MaoBui2907

VNStock MCP Server

by MaoBui2907

get_balance_sheets

Obtain balance sheets for Vietnamese stocks. Input company symbol, choose period (quarter/year), and select output format (JSON, dataframe, or toon).

Instructions

Get balance sheets of a company from stock market Args: symbol: str (symbol of the company to get balance sheets) period: Literal['quarter', 'year'] = 'year' (period to get balance sheets) 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, the description must disclose behavioral traits, but it only states the basic function. It fails to mention side effects (none expected), rate limits, historical depth, or that it returns a DataFrame. The output format hint ('toon' optimized for AI) is helpful but insufficient.

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 concise with the main purpose front-loaded. The structured Args and Returns sections aid readability, though the parameter descriptions could be integrated more succinctly without losing 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?

Without an output schema, the description should explain the returned data structure. It only states 'Returns: pd.DataFrame', which is insufficient for a complex financial report. No information is given about typical column names, date ranges, or data volume.

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 coverage is 0%, so the description must compensate. It adds minimal context: symbol is explained as 'symbol of the company', and output_format clarifies 'toon' is AI-optimized. However, period's description ('period to get balance sheets') is tautological, and all enums are already in the schema.

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 it retrieves balance sheets for a company, which distinguishes it from sibling tools like get_income_statements. However, it lacks specificity about scope (e.g., latest or historical) and could be more explicit in differentiating from similar financial statement 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 such as get_cash_flows or get_income_statements. The description omits context about prerequisites, typical use cases, or scenarios where this tool is preferred.

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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