mcp_massive MCP — 사용 사례, 설치 & 라이브 데모

왜 쓰나요

핵심 기능

Dynamic endpoint discovery — no hardcoded tool list for 100+ API endpoints
DataFrame storage with in-memory SQL — chain calls without token explosion
Built-in: Black-Scholes, return calcs, technical indicators
Covers stocks, options, forex, crypto, futures, real-time trades, fundamentals
Install via uv tool install; Python 3.12+

라이브 데모

실제 사용 모습

massive.replay ▶ 준비됨

0/0

설치

클라이언트 선택

~/Library/Application Support/Claude/claude_desktop_config.json · Windows: %APPDATA%\Claude\claude_desktop_config.json

{
  "mcpServers": {
    "massive": {
      "command": "uvx",
      "args": [
        "mcp_massive"
      ],
      "_inferred": true
    }
  }
}

Claude Desktop → Settings → Developer → Edit Config 열기. 저장 후 앱 재시작.

~/.cursor/mcp.json · .cursor/mcp.json

{
  "mcpServers": {
    "massive": {
      "command": "uvx",
      "args": [
        "mcp_massive"
      ],
      "_inferred": true
    }
  }
}

Cursor는 Claude Desktop과 동일한 mcpServers 스키마 사용. 프로젝트 설정이 전역보다 우선.

VS Code → Cline → MCP Servers → Edit

{
  "mcpServers": {
    "massive": {
      "command": "uvx",
      "args": [
        "mcp_massive"
      ],
      "_inferred": true
    }
  }
}

Cline 사이드바의 MCP Servers 아이콘 클릭 후 "Edit Configuration" 선택.

~/.codeium/windsurf/mcp_config.json

{
  "mcpServers": {
    "massive": {
      "command": "uvx",
      "args": [
        "mcp_massive"
      ],
      "_inferred": true
    }
  }
}

Claude Desktop과 같은 형식. Windsurf 재시작 후 적용.

~/.continue/config.json

{
  "mcpServers": [
    {
      "name": "massive",
      "command": "uvx",
      "args": [
        "mcp_massive"
      ]
    }
  ]
}

Continue는 맵이 아닌 서버 오브젝트 배열 사용.

~/.config/zed/settings.json

{
  "context_servers": {
    "massive": {
      "command": {
        "path": "uvx",
        "args": [
          "mcp_massive"
        ]
      }
    }
  }
}

context_servers에 추가. 저장 시 Zed가 핫 리로드.

claude mcp add massive -- uvx mcp_massive

한 줄 명령. claude mcp list로 확인, claude mcp remove로 제거.

사용 사례

실전 활용법: mcp_massive

Analyze an options chain and price theoretical value

👤 Options traders, quant hobbyists ⏱ ~20 min advanced

언제 쓸까: You want to look at AAPL options, compare IV to historical vol, and spot-check Black-Scholes values.

사전 조건

Massive.com API key — massive.com signup — free tier

흐름

Find the right endpoint

Search endpoints: 'options chain for a given ticker and expiry'.✓ 복사됨

→ Best-match endpoint + params
Pull chain

Call the endpoint for AAPL with the next monthly expiry. Store as DataFrame 'aapl_chain'.✓ 복사됨

→ DataFrame stored; head preview
Analyze

Run SQL: SELECT strike, iv, bid, ask FROM aapl_chain WHERE expiry=... ORDER BY strike. Price Black-Scholes for each and show deltas to market mid.✓ 복사됨

→ Table of strikes with BS-vs-market

결과: Hands-on options screen without writing Python yourself.

함정

Delayed data on free tier — Check data timestamps; paid tier for real-time

Compute total return for a portfolio over a period

👤 Investors tracking performance ⏱ ~15 min intermediate

언제 쓸까: Year-end — how did my 10-position portfolio perform vs SPY?

흐름

Fetch prices

For each ticker in [list], get daily close prices from Jan 1 to today. Store each as a DataFrame.✓ 복사됨

→ Price series per ticker
Compute weighted return

Using my weights [paste], compute weighted portfolio return. Compare to SPY return over the same period.✓ 복사됨

→ Portfolio YTD vs SPY YTD

결과: Quick portfolio report without spreadsheets.

함정

No dividend reinvestment accounting by default — Use adjusted-close series; ask Claude to note the assumption

함께 쓰기: filesystem

Screen stocks on fundamental criteria

👤 Value investors ⏱ ~20 min intermediate

언제 쓸까: You want US large-cap stocks with P/E < 15, dividend yield > 3%, and positive 5y earnings growth.

흐름

Find endpoints

Search endpoints for 'fundamentals' and 'screener'. Pick the right one.✓ 복사됨

→ Endpoint choice
Screen

Pull fundamentals for S&P 500 components. Store as DataFrame. Run SQL to filter my criteria.✓ 복사됨

→ Matching tickers with key ratios

결과: A shortlist to research further.

함정

Fundamentals data lags quarterly filings by weeks — Check as_of dates

조합

다른 MCP와 조합해 10배 효율

massive + filesystem

Persist analysis to CSV for Excel follow-up

After the screen, export the final DataFrame as /reports/screen-YYYY-MM-DD.csv.✓ 복사됨

도구

이 MCP가 노출하는 것

도구	입력	언제 호출	비용
search_endpoints	query: str	First step — discover what's available	free
get_endpoint_docs	endpoint_id	Before calling an endpoint you don't know	free
call_api	endpoint, params, store_as?: str	Actually fetch data; store to re-query	1 API call
query_data	sql: str	Analyze stored DataFrames without re-fetching	free

비용 및 제한

운영 비용

API 쿼터: Per Massive.com plan
호출당 토큰: Raw series can be large — use DataFrame + SQL to slice
금액: Free tier exists; paid for real-time / higher volume
팁: store_as + query_data is dramatically cheaper than re-fetching and re-parsing each time.

보안

권한, 시크릿, 파급범위

자격 증명 저장: MASSIVE_API_KEY env var

데이터 외부 송신: Queries to massive.com

Project is marked experimental; breaking API changes possible. Pin version.
Financial data is for info only — this is not investment advice and the LLM can miscompute.

문제 해결

자주 발생하는 오류와 해결

401 from Massive

Check API key. Verify it's still active in the dashboard.

DataFrame limit exceeded (50 / 50k rows default)

Raise via env config or chunk via SQL limits.

SQL syntax error in query_data

Query engine is a subset — check docs for supported functions.

대안

mcp_massive 다른 것과 비교

대안	언제 쓰나	단점/장점
Alpaca MCP	You want trading too, not just data	Different broker; narrower data
Polygon.io / Alpha Vantage direct + fetch MCP	You want full API control	You write the fetching and parsing yourself

mcp_massive