MCP Server for Korean stock analysis with 6 specialized tools covering price data, technical indicators, chart patterns, financial statements, news sentiment, and macro indicators
This stock analysis MCP server provides 6 tools with complete input schemas and descriptions in Korean. Naming follows verb_noun convention appropriately (get_*, analyze_*). Descriptions are substantive (150-250 chars average) and explain both what each tool does and when to use it. However, OUTPUT SCHEMAS ARE COMPLETELY UNDOCUMENTED, the server returns JSON but does not publish what fields agents should expect. Error handling is minimal (generic try-catch with 'error' field only). No parameter descriptions for numeric constraints like 'days' min/max are enforced beyond JSON Schema. Tool composition is reasonable for a domain-specific server, though some tools could be split (e.g., analyze_chart_pattern returns both patterns AND 60-candle OHLCV data, mixing concerns). The server is STDIO-only, which hard-caps protocol readiness at 50.
주요 차트 패턴(Double Bottom, 역 헤드앤숄더, 박스권 돌파, 삼각수렴)을 탐지합니다. 패턴 신뢰도와 함께 최근 60봉 OHLCV 데이터를 반환합니다.
pykrx로 PER, PBR, EPS, 배당수익률 등 Fundamental 지표를 조회합니다. EPS 추세 분석으로 실적 모멘텀을 평가합니다.
글로벌 매크로 지표를 수집합니다: USD/KRW 환율 추세, KOSPI/KOSDAQ 지수 현황, 외국인 수급 동향. 지정학적 리스크와 글로벌 증시 흐름이 한국 주식에 미치는 영향 분석에 사용합니다.
Naver Finance에서 최근 종목 뉴스를 스크래핑하고 감성 점수를 계산합니다. 긍정/부정 기사 수, 주요 헤드라인, 전체 기사 목록을 반환합니다. 매크로(환율/금리/글로벌 증시) 내용은 get_macro_indicators를 사용하세요.
한국 주식 OHLCV 데이터를 pykrx로 조회합니다. 종목 코드와 기간을 입력하면 최신 가격, 등락률, 거래량 히스토리를 반환합니다.
RSI, MACD, 볼린저밴드, 거래량 비율을 계산합니다. pandas-ta를 사용하며 각 지표에 대한 신호 해석(과매도/골든크로스 등)을 포함합니다.
No output schemas documented. Tools return JSON but agents cannot plan downstream operations without knowing response structure (e.g., field names, types, pagination). Baselines show 100% of A+ tools have documented return types.
Error handling is minimal and non-actionable. Generic catch block returns {'error': str(e)} without categorizing errors as retryable, user-fixable, or fatal. Agents cannot determine whether to retry, ask user, or give up.
| Scored | Grade | Overall | Spec posture | Rubric |
|---|---|---|---|---|
| 2026-09-22 | F | 0 | <=2025-11-25 | v2 |
| 2026-03-09 | D | 55 | - | v1 |
Parameter constraints (e.g., 'days' has min=1, max=30) are declared in JSON Schema but descriptions do not explicitly state the range in natural language. LLMs cannot reliably read JSON Schema constraints, they rely on description text. For 'get_macro_indicators', days has no lower bound in the required array but min=7 in schema, this inconsistency is confusing.
The tool 'analyze_chart_pattern' mixes multiple concerns: detecting patterns AND returning 60-candle OHLCV data. Agents do not always need both, splitting into 'analyze_chart_pattern' (pattern detection + confidence only) and 'get_ohlcv_history' (raw candles) would better follow single-responsibility principle.
Tool descriptions are in Korean only. While domain-appropriate for a Korean stock market server, this limits discoverability and usability for non-Korean-speaking users and cross-border agent frameworks that expect English metadata.