Schema | VNStock MCP Server

VNStock MCP Server

Overview Schema Related Servers Score Discussions

Server Configuration

Describes the environment variables required to run the server.

Name	Required	Description	Default
No arguments

Instructions

Guidance the server publishes about itself, which clients place ahead of the tool catalog so the model reads it before choosing anything.

This server publishes no instructions, or was last inspected before Glama recorded them.

Capabilities

Features and capabilities supported by this server

Protocol revision2025-11-25

Capability	Details
`tools`	{ "listChanged": true }
`logging`	{}
`prompts`	{ "listChanged": false }
`resources`	{ "subscribe": false, "listChanged": false }
`extensions`	{ "io.modelcontextprotocol/ui": {} }
`experimental`	{}

Tools

Functions exposed to the LLM to take actions

Name	Description
list_all_icb_industriesB	List all ICB industries from stock market Args: output_format: Literal['json', 'dataframe', 'toon'] = 'toon' (output format, 'toon' is optimized for AI) Returns: pd.DataFrame
list_all_companies_with_detailsB	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
get_company_overviewC	Get company overview from stock market Args: symbol: str output_format: Literal['json', 'dataframe', 'toon'] = 'toon' (output format, 'toon' is optimized for AI) Returns: pd.DataFrame
get_company_newsA	Get company news from stock market Args: symbol: str page_size: int = 10 page: int = 0 output_format: Literal['json', 'dataframe', 'toon'] = 'toon' (output format, 'toon' is optimized for AI) Returns: pd.DataFrame
get_company_eventsC	Get company events from stock market Args: symbol: str page_size: int = 10 page: int = 0 output_format: Literal['json', 'dataframe', 'toon'] = 'toon' (output format, 'toon' is optimized for AI) Returns: pd.DataFrame
get_company_shareholdersC	Get company shareholders from stock market Args: symbol: str output_format: Literal['json', 'dataframe', 'toon'] = 'toon' (output format, 'toon' is optimized for AI) Returns: pd.DataFrame
get_company_officersB	Get company officers from stock market Args: symbol: str filter_by: Literal['working', "all", 'resigned'] = 'working' output_format: Literal['json', 'dataframe', 'toon'] = 'toon' (output format, 'toon' is optimized for AI) Returns: pd.DataFrame
get_company_subsidiariesA	Get company subsidiaries from stock market Args: symbol: str filter_by: Literal["all", "subsidiary"] = "all" output_format: Literal['json', 'dataframe', 'toon'] = 'toon' (output format, 'toon' is optimized for AI) Returns: pd.DataFrame
get_company_reportsB	Get company reports from stock market Args: symbol: str output_format: Literal['json', 'dataframe', 'toon'] = 'toon' (output format, 'toon' is optimized for AI) Returns: pd.DataFrame
get_company_dividendsB	Get company dividends from stock market Args: symbol: str output_format: Literal['json', 'dataframe', 'toon'] = 'toon' (output format, 'toon' is optimized for AI) Returns: pd.DataFrame
get_company_insider_dealsB	Get company insider deals from stock market Args: symbol: str output_format: Literal['json', 'dataframe', 'toon'] = 'toon' (output format, 'toon' is optimized for AI) Returns: pd.DataFrame
get_company_ratio_summaryC	Get company ratio summary from stock market Args: symbol: str output_format: Literal['json', 'dataframe', 'toon'] = 'toon' (output format, 'toon' is optimized for AI) Returns: pd.DataFrame
get_company_trading_statsB	Get company trading stats from stock market Args: symbol: str output_format: Literal['json', 'dataframe', 'toon'] = 'toon' (output format, 'toon' is optimized for AI) Returns: pd.DataFrame
get_all_symbol_groupsB	Get all symbol groups from stock market Args: output_format: Literal['json', 'dataframe', 'toon'] = 'toon' (output format, 'toon' is optimized for AI) Returns: pd.DataFrame
get_all_symbols_by_groupC	Get all symbols from stock market Args: group: str (group name to get symbols) output_format: Literal['json', 'dataframe', 'toon'] = 'toon' (output format, 'toon' is optimized for AI) Returns: pd.DataFrame
get_all_symbols_by_industryA	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
get_all_symbolsB	Get all symbols from stock market Args: output_format: Literal['json', 'dataframe', 'toon'] = 'toon' (output format, 'toon' is optimized for AI) Returns: pd.DataFrame or json
get_all_symbols_detailedB	Get all symbols detailed from stock market Args: output_format: Literal['json', 'dataframe', 'toon'] = 'toon' (output format, 'toon' is optimized for AI) Returns: pd.DataFrame
get_income_statementsB	Get income statements of a company from stock market Args: symbol: str (symbol of the company to get income statements) period: Literal['quarter', 'year'] = 'year' (period to get income statements) output_format: Literal['json', 'dataframe', 'toon'] = 'toon' (output format, 'toon' is optimized for AI) Returns: pd.DataFrame
get_balance_sheetsC	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
get_cash_flowsA	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
get_finance_ratiosB	Get finance ratios of a company from stock market Args: symbol: str (symbol of the company to get finance ratios) period: Literal['quarter', 'year'] = 'year' (period to get finance ratios) output_format: Literal['json', 'dataframe', 'toon'] = 'toon' (output format, 'toon' is optimized for AI) Returns: pd.DataFrame
get_raw_reportB	Get raw report of a company from stock market Args: symbol: str (symbol of the company to get raw report) period: Literal['quarter', 'year'] = 'year' (period to get raw report) output_format: Literal['json', 'dataframe', 'toon'] = 'toon' (output format, 'toon' is optimized for AI) Returns: pd.DataFrame
list_all_fundsA	List all funds from stock market Args: fund_type: Literal['BALANCED', 'BOND', 'STOCK', None ] = None (if None, return funds in all types) output_format: Literal['json', 'dataframe', 'toon'] = 'toon' (output format, 'toon' is optimized for AI) Returns: pd.DataFrame
search_fundA	Search fund by name from stock market Args: keyword: str (partial match for fund name to search) output_format: Literal['json', 'dataframe', 'toon'] = 'toon' (output format, 'toon' is optimized for AI) Returns: pd.DataFrame
get_fund_nav_reportB	Get nav report of a fund from stock market Args: symbol: str (symbol of the fund to get nav report) output_format: Literal['json', 'dataframe', 'toon'] = 'toon' (output format, 'toon' is optimized for AI) Returns: pd.DataFrame
get_fund_top_holdingA	Get top holding of a fund from stock market Args: symbol: str (symbol of the fund to get top holding) output_format: Literal['json', 'dataframe', 'toon'] = 'toon' (output format, 'toon' is optimized for AI) Returns: pd.DataFrame
get_fund_industry_holdingB	Get industry holding of a fund from stock market Args: symbol: str (symbol of the fund to get industry holding) output_format: Literal['json', 'dataframe', 'toon'] = 'toon' (output format, 'toon' is optimized for AI) Returns: pd.DataFrame
get_fund_asset_holdingB	Get asset holding of a fund from stock market Args: symbol: str (symbol of the fund to get asset holding) output_format: Literal['json', 'dataframe', 'toon'] = 'toon' (output format, 'toon' is optimized for AI) Returns: pd.DataFrame
get_gold_priceB	Get gold price from stock market Args: date: str = None (if None, return today's price. Format: YYYY-MM-DD) source: Literal['SJC', 'BTMC'] = 'SJC' (source to get gold price) output_format: Literal['json', 'dataframe', 'toon'] = 'toon' (output format, 'toon' is optimized for AI) Returns: pd.DataFrame
get_exchange_rateB	Get exchange rate of all currency pairs from stock market Args: date: str = None (if None, return today's price. Format: YYYY-MM-DD) output_format: Literal['json', 'dataframe', 'toon'] = 'toon' (output format, 'toon' is optimized for AI) Returns: pd.DataFrame
get_quote_price_with_indicatorsB	Get quote price with indicators of a symbol from stock market. Indicators can be specified with or without parameters: Simple: "rsi", "macd", "stochastic" With params: "rsi(window=21)", "macd(fast=12, slow=26, signal=9)"
get_quote_history_priceB	Get quote price history of a symbol from stock market Args: symbol: str (symbol to get history price) start_date: str (format: YYYY-MM-DD) end_date: str = None (end date to get history price. None means today) interval: Literal['1m', '5m', '15m', '30m', '1H', '1D', '1W', '1M'] = '1D' (interval to get history price) output_format: Literal['json', 'dataframe', 'toon'] = 'toon' (output format, 'toon' is optimized for AI) Returns: pd.DataFrame
get_quote_intraday_priceC	Get quote intraday price from stock market Args: symbol: str (symbol to get intraday price) page_size: int = 500 (max: 100000) (number of rows to return) page: int = 1 (page number to get intraday price from) output_format: Literal['json', 'dataframe', 'toon'] = 'toon' (output format, 'toon' is optimized for AI) Returns: pd.DataFrame
get_quote_price_depthA	Get quote price depth from stock market Args: symbol: str (symbol to get price depth) output_format: Literal['json', 'dataframe', 'toon'] = 'toon' (output format, 'toon' is optimized for AI) Returns: pd.DataFrame
get_price_boardB	Get price board from stock market Args: symbols: list[str] (list of symbols to get price board) output_format: Literal['json', 'dataframe', 'toon'] = 'toon' (output format, 'toon' is optimized for AI) Returns: pd.DataFrame

Prompts

Interactive templates invoked by user choice

Name	Description
No prompts

Resources

Contextual data attached and managed by the client

Name	Description
`_list_available_indicators`	List all available indicators. Returns list of dicts with name, description, parameters, output_columns, and usage.
`_get_available_indicators_detailed`	Get list of all available indicators with detailed information. Returns list of dicts with name, description, parameters, output_columns, and usage.

Server Configuration
Instructions
Capabilities
Tools
Prompts
Resources

Latest Blog Posts

Who's Calling? MCP Hosts Are an Identity Blind Spot (And the Spec Knows It)
By Om-Shree-0709 on July 25, 2026.
mcp
Agent Identity
OAuth 2.1
Your AI Chatbot Just Exposed Your CEO's Salary to an Intern
By Om-Shree-0709 on July 2, 2026.
Agent Identity
MCP Security
OAuth Delegation
Why MCP Servers Need Execution Sandboxing (And Why Your Current Stack Isn't Enough)
By Om-Shree-0709 on June 30, 2026.
Agentic Ai
Prompt Injection
WebAssembly

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/MaoBui2907/vnstock-mcp-server'

If you have feedback or need assistance with the MCP directory API, please join our Discord server