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MaoBui2907

VNStock MCP Server

by MaoBui2907

list_all_companies_with_details

Retrieve all listed companies on the Vietnamese stock market with detailed information. Supports JSON, dataframe, and AI-optimized output formats.

Instructions

List all companies from stock market with details Args: output_format: Literal['json', 'dataframe', 'toon'] = 'toon' (output format, 'toon' is optimized for AI) Returns: pd.DataFrame

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
output_formatNotoon
Behavior2/5

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

With no annotations, the description carries the full burden. It does not disclose any behavioral traits such as side effects, permissions, rate limits, or that it is a read-only operation. The return type is mentioned but not the nature of the data.

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 extremely concise, with a clear front-loaded purpose and well-structured args/returns section. Every sentence adds value with no unnecessary words.

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?

The description adequately covers the parameter and return type, but lacks context on what 'details' means in the output. Given no output schema and many siblings, more details on the return content 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 description explains the single parameter `output_format` well, including its options and the custom 'toon' format optimized for AI. This adds meaning beyond the schema, which lacks descriptions.

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 first sentence clearly states that the tool lists all companies with details. However, it does not differentiate from sibling tools like `get_all_symbols` or `get_all_symbols_detailed`, which may have similar functionality.

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. It simply states what the tool does without any context on prerequisites or comparisons to sibling tools.

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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