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ariesanhthu

VNStock MCP Server

by ariesanhthu
OverviewSchemaRelated ServersScoreDiscussions
Python
Hybrid

get_all_symbols_by_group

Retrieve all stock symbols from Vietnam's market by group category, returning data in JSON or DataFrame format for analysis.

Instructions

Get all symbols from stock market
Args:
    group: str (group name to get symbols)
    output_format: Literal['json', 'dataframe'] = 'json'
Returns:
    pd.DataFrame

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
groupYes
output_formatNojson

Implementation Reference

  • src/vnstock_mcp/server.py:313-331 (handler)
    The handler function for the get_all_symbols_by_group tool. It is registered via the @server.tool() decorator and implements the core logic using VCIListing to fetch symbols by group, supporting JSON or DataFrame output.
    @server.tool()
def get_all_symbols_by_group(
    group: str, output_format: Literal["json", "dataframe"] = "json"
):
    """
    Get all symbols from stock market
    Args:
        group: str (group name to get symbols)
        output_format: Literal['json', 'dataframe'] = 'json'
    Returns:
        pd.DataFrame
    """
    listing = VCIListing()
    df = listing.symbols_by_group(group=group)
    if output_format == "json":
        return df.to_json(orient="records", force_ascii=False)
    else:
        return df
  • src/vnstock_mcp/server.py:313-313 (registration)
    The @server.tool() decorator registers the get_all_symbols_by_group function as an MCP tool.
    @server.tool()
  • src/vnstock_mcp/server.py:315-322 (schema)
    Type hints and docstring define the input schema (group: str, output_format: Literal) and output (pd.DataFrame or JSON).
        group: str, output_format: Literal["json", "dataframe"] = "json"
):
    """
    Get all symbols from stock market
    Args:
        group: str (group name to get symbols)
        output_format: Literal['json', 'dataframe'] = 'json'
    Returns:

Tool Definition Quality

C2.8/5.0
Behavior2/5

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It mentions the return type (pd.DataFrame) but lacks critical details like whether this is a read-only operation, potential rate limits, authentication requirements, or error conditions. For a data retrieval tool with zero annotation coverage, this is insufficient.

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

Conciseness3/5

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

The description is brief but structured with sections for Args and Returns. However, the first sentence is vague ('from stock market'), and the formatting is inconsistent (mixing str/Literal notation with plain text). It could be more front-loaded with clearer purpose.

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?

Given no annotations, no output schema, and low schema description coverage (0%), the description is incomplete. It misses behavioral context (e.g., safety, performance), detailed parameter semantics, and doesn't clarify the relationship to sibling tools. For a 2-parameter tool in a financial data context, this leaves significant gaps.

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 description coverage is 0%, so the description must compensate. It documents both parameters ('group' and 'output_format') and provides the return type, but doesn't explain what 'group' means (e.g., sector, exchange) or the implications of choosing 'json' vs 'dataframe'. This adds some value but leaves key semantics unclear.

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 action ('Get all symbols') and resource ('from stock market'), with the 'by group' qualifier indicating filtering. However, it doesn't explicitly differentiate from sibling tools like 'get_all_symbols' or 'get_all_symbols_by_industry', which would require a 5.

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 like 'get_all_symbols' or 'get_all_symbols_by_industry'. The description only states what it does, not when it's appropriate.

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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