VNStock MCP Server

get_all_symbols_by_industry

Get all stock symbols from the Vietnam market, optionally filtered by industry. Output formats include JSON, dataframe, or an AI-optimized format.

Instructions

Get all symbols from stock market Args: industry: str = None (if None, return all symbols) output_format: Literal['json', 'dataframe', 'toon'] = 'toon' (output format, 'toon' is optimized for AI) Returns: pd.DataFrame or json

Tool call template

This tool declares no required parameters. Add optional parameters from the input schema as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {},
    "name": "get_all_symbols_by_industry"
  }
}

Input Schema

Name	Required	Description	Default
`industry`	No
`output_format`	No		toon

Schema Changelog

Changes observed during successful MCP inspections.

First observedv1.0.4Jun 21, 2026

TDQS

A3.5/5.0

Behavior3/5

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

No annotations are provided, so the description must cover behavioral traits. It mentions return types (pd.DataFrame or json) and output_format options, but does not explain side effects, permissions, or the exact nature of the 'toon' format beyond being optimized for AI.

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 and uses a clear docstring format. However, it could be slightly more streamlined, as the args section repeats information already in the schema.

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 the tool has 2 optional parameters and no output schema, the description covers purpose and returns but lacks details on data structure limits or how the output format affects the result. It also does not differentiate sufficiently from similar sibling tools.

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?

With 0% schema coverage, the description fully explains both parameters: industry defaults to None (all symbols), and output_format with 'toon' optimized for AI. This adds meaningful context beyond the schema's enum list.

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 the tool retrieves all symbols from the stock market, with optional industry filtering. However, it does not differentiate from the sibling tool 'get_all_symbols', which likely returns all symbols without industry filtering, causing potential confusion.

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 mentions that setting industry to None returns all symbols, providing basic guidance. However, it does not compare to siblings like 'get_all_symbols' or 'get_all_symbols_by_group', nor does it specify when to use this tool over alternatives.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.