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dragon1086

kospi-kosdaq

by dragon1086
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Remote

Server Configuration

Describes the environment variables required to run the server.

NameRequiredDescriptionDefault

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

Server capabilities have not been inspected yet.

Tools

Functions exposed to the LLM to take actions

NameDescription
load_all_tickersA

Loads all ticker symbols and names for KOSPI and KOSDAQ into memory.

Returns:
    Dict[str, str]: A dictionary mapping tickers to stock names.
    Example: {"005930": "삼성전자", "035720": "카카오", ...}
get_stock_ohlcvA

Retrieves OHLCV (Open/High/Low/Close/Volume) data for a specific stock.

Args:
    fromdate (str): Start date for retrieval (YYYYMMDD)
    todate   (str): End date for retrieval (YYYYMMDD)
    ticker   (str): Stock ticker symbol
    adjusted (bool, optional): Whether to use adjusted prices (True: adjusted, False: unadjusted). Defaults to True.

Returns:
    DataFrame:
        >> get_stock_ohlcv("20210118", "20210126", "005930")
                        Open     High     Low    Close   Volume
        Date
        2021-01-26  89500  94800  89500  93800  46415214
        2021-01-25  87300  89400  86800  88700  25577517
        2021-01-22  89000  89700  86800  86800  30861661
        2021-01-21  87500  88600  86500  88100  25318011
        2021-01-20  89000  89000  86500  87200  25211127
        2021-01-19  84500  88000  83600  87000  39895044
        2021-01-18  86600  87300  84100  85000  43227951
get_stock_market_capA

Retrieves market capitalization data for a specific stock.

Args:
    fromdate (str): Start date for retrieval (YYYYMMDD)
    todate   (str): End date for retrieval (YYYYMMDD)
    ticker   (str): Stock ticker symbol

Returns:
    DataFrame:
        >> get_stock_market_cap("20150720", "20150724", "005930")
                          Market Cap  Volume      Trading Value  Listed Shares
        Date
        2015-07-24  181030885173000  196584  241383636000  147299337
        2015-07-23  181767381858000  208965  259446564000  147299337
        2015-07-22  184566069261000  268323  333813094000  147299337
        2015-07-21  186039062631000  194055  244129106000  147299337
        2015-07-20  187806654675000  128928  165366199000  147299337
get_stock_fundamentalA

Retrieves fundamental data (PER/PBR/Dividend Yield) for a specific stock.

Args:
    fromdate (str): Start date for retrieval (YYYYMMDD)
    todate   (str): End date for retrieval (YYYYMMDD)
    ticker   (str): Stock ticker symbol

Returns:
    DataFrame:
        >> get_stock_fundamental("20210104", "20210108", "005930")
                          BPS        PER       PBR   EPS       DIV   DPS
            Date
            2021-01-08  37528  28.046875  2.369141  3166  1.589844  1416
            2021-01-07  37528  26.187500  2.210938  3166  1.709961  1416
            2021-01-06  37528  25.953125  2.189453  3166  1.719727  1416
            2021-01-05  37528  26.500000  2.240234  3166  1.690430  1416
            2021-01-04  37528  26.218750  2.210938  3166  1.709961  1416
get_stock_trading_volumeA

Retrieves trading volume by investor type for a specific stock.

Args:
    fromdate (str): Start date for retrieval (YYYYMMDD)
    todate   (str): End date for retrieval (YYYYMMDD)
    ticker   (str): Stock ticker symbol

Returns:
    DataFrame with columns:
    - Volume (Sell/Buy/Net Buy)
    - Trading Value (Sell/Buy/Net Buy)
    Broken down by investor types (Financial Investment, Insurance, Trust, etc.)
get_index_ohlcvA

Retrieves OHLCV data for a specific index.

Args:
    fromdate (str): Start date for retrieval (YYYYMMDD)
    todate   (str): End date for retrieval (YYYYMMDD)
    ticker   (str): Index ticker symbol (e.g., 1001 for KOSPI, 2001 for KOSDAQ)
    freq     (str, optional): d - daily / m - monthly / y - yearly. Defaults to 'd'.

Returns:
    DataFrame:
        >> get_index_ohlcv("20210101", "20210130", "1001")
                       Open     High      Low    Close       Volume    Trading Value
        Date
        2021-01-04  2874.50  2946.54  2869.11  2944.45  1026510465  25011393960858
        2021-01-05  2943.67  2990.57  2921.84  2990.57  1519911750  26548380179493
        2021-01-06  2993.34  3027.16  2961.37  2968.21  1793418534  29909396443430
        2021-01-07  2980.75  3055.28  2980.75  3031.68  1524654500  27182807334912
        2021-01-08  3040.11  3161.11  3040.11  3152.18  1297903388  40909490005818

Prompts

Interactive templates invoked by user choice

NameDescription
search_stock_data_promptPrompt template for searching stock data.
get_stock_data_promptPrompt template for retrieving stock data.

Resources

Contextual data attached and managed by the client

NameDescription
stock://tickers
stock://format-guide
stock://popular-tickers
Deploy Server
One endpoint for every MCP toolRayrun runs every MCP server behind one URL and holds the credentials. Each call is decided per client, tool, and argument, then recorded.ray.runAd

TDQS

A4.1/5.0

Scored across 6 tools

Disambiguation5/5

Each tool has a clearly distinct purpose with no overlap. get_index_ohlcv retrieves index data, get_stock_ohlcv retrieves stock price data, get_stock_fundamental provides fundamental metrics, get_stock_market_cap gives market capitalization, get_stock_trading_volume shows investor breakdowns, and load_all_tickers provides ticker metadata. The separation between index vs. stock tools and different data types is unambiguous.

Naming Consistency5/5

All tools follow a consistent verb_noun pattern with 'get_' or 'load_' prefixes. The naming is perfectly uniform: get_index_ohlcv, get_stock_fundamental, get_stock_market_cap, get_stock_ohlcv, get_stock_trading_volume, and load_all_tickers. This consistency makes the tool set predictable and easy to understand.

Tool Count5/5

With 6 tools, this server is well-scoped for financial data retrieval. Each tool serves a specific, necessary function for stock and index analysis without redundancy. The count is ideal for covering core data needs (price, fundamentals, market cap, volume breakdowns, and ticker metadata) without being overwhelming or insufficient.

Completeness4/5

The tool set covers essential data retrieval for Korean stock market analysis comprehensively, including price data, fundamentals, market cap, trading insights, and ticker information. A minor gap exists in lacking tools for real-time data or more advanced analytics like technical indicators, but the core CRUD-like retrieval operations for the domain are well-covered.

Maintenance

ActivityMaintained
ResponsivenessUnresponsive

Resources