KOSPI/KOSDAQ Stock Server

The Kospi/Kosdaq Stock Server is a lightweight MCP (Model Context Protocol) service that exposes comprehensive Korean equity market data to AI assistants such as Claude. By running a FastMCP server, it offers developers a ready‑to‑use API for retrieving ticker information, price history, market capitalisation, fundamental ratios, and investor‑type trading volumes—all without the need for manual data scraping or API key management. This eliminates a common bottleneck in financial AI workflows: obtaining clean, up‑to‑date market data from disparate sources.

Developers can leverage the server in several ways. First, it simplifies access to KOSPI and KOSDAQ tickers: a single call loads all symbols into memory, enabling quick look‑ups or autocomplete features in conversational agents. Second, the server provides time‑series OHLCV data with an optional “adjusted” flag, letting models generate accurate price trends or compute technical indicators. Third, it supplies fundamental metrics (PER, PBR, dividend yield) and market‑cap figures that enable valuation analyses or portfolio construction. Finally, the trading‑volume breakdown by investor type (institutional, retail, foreign) gives AI assistants deeper insights into market sentiment and liquidity dynamics.

The toolset is intentionally straightforward: each function accepts a ticker symbol and a date range, returning structured data that can be parsed directly into charts or statistical models. For example, a financial chatbot could answer “What was the closing price of Samsung Electronics on 2023‑05‑15?” by invoking . Or a portfolio manager might ask “Show me the PER trend for Hyundai Motor from 2022‑01‑01 to 2023‑12‑31,” which the server supplies instantly.

Integrating this MCP into an AI workflow is seamless. Once the server is running, any Claude instance can call its tools via the MCP interface—no additional authentication or rate‑limit handling is required. This allows developers to build sophisticated, data‑driven financial assistants that can fetch real‑time market information, perform calculations on the fly, and deliver actionable insights in natural language. The server’s design prioritises reliability (using uv for isolated execution) and minimal overhead, making it an attractive component for production‑grade AI finance applications.