mcp_massive MCP — Anwendungsfälle, Installieren & Live-Demo

Warum nutzen

Hauptfunktionen

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+

Live-Demo

In der Praxis

massive.replay ▶ bereit

0/0

Installieren

Wählen Sie Ihren Client

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

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

Öffne Claude Desktop → Settings → Developer → Edit Config. Nach dem Speichern neu starten.

~/.cursor/mcp.json · .cursor/mcp.json

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

Cursor nutzt das gleiche mcpServers-Schema wie Claude Desktop. Projektkonfiguration schlägt die globale.

VS Code → Cline → MCP Servers → Edit

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

Klicken Sie auf das MCP-Servers-Symbol in der Cline-Seitenleiste, dann "Edit Configuration".

~/.codeium/windsurf/mcp_config.json

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

Gleiche Struktur wie Claude Desktop. Windsurf neu starten zum Übernehmen.

~/.continue/config.json

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

Continue nutzt ein Array von Serverobjekten statt einer Map.

~/.config/zed/settings.json

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

In context_servers hinzufügen. Zed lädt beim Speichern neu.

claude mcp add massive -- uvx mcp_massive

Einzeiler. Prüfen mit claude mcp list. Entfernen mit claude mcp remove.

Anwendungsfälle

Praxisnahe Nutzung: mcp_massive

Analyze an options chain and price theoretical value

👤 Options traders, quant hobbyists ⏱ ~20 min advanced

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

Voraussetzungen

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

Ablauf

Find the right endpoint

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

→ Best-match endpoint + params
Pull chain

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

→ 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.✓ Kopiert

→ Table of strikes with BS-vs-market

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

Fallstricke

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

Wann einsetzen: Year-end — how did my 10-position portfolio perform vs SPY?

Ablauf

Fetch prices

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

→ Price series per ticker
Compute weighted return

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

→ Portfolio YTD vs SPY YTD

Ergebnis: Quick portfolio report without spreadsheets.

Fallstricke

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

Kombinieren mit: filesystem

Screen stocks on fundamental criteria

👤 Value investors ⏱ ~20 min intermediate

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

Ablauf

Find endpoints

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

→ Endpoint choice
Screen

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

→ Matching tickers with key ratios

Ergebnis: A shortlist to research further.

Fallstricke

Fundamentals data lags quarterly filings by weeks — Check as_of dates

Kombinationen

Mit anderen MCPs für 10-fache Wirkung

massive + filesystem

Persist analysis to CSV for Excel follow-up

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

Werkzeuge

Was dieses MCP bereitstellt

Werkzeug	Eingaben	Wann aufrufen	Kosten
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

Kosten & Limits

Was der Betrieb kostet

API-Kontingent: Per Massive.com plan
Tokens pro Aufruf: Raw series can be large — use DataFrame + SQL to slice
Kosten in €: Free tier exists; paid for real-time / higher volume
Tipp: store_as + query_data is dramatically cheaper than re-fetching and re-parsing each time.

Sicherheit

Rechte, Secrets, Reichweite

Credential-Speicherung: MASSIVE_API_KEY env var

Datenabfluss: 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.

Fehlerbehebung

Häufige Fehler und Lösungen

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.

Alternativen

mcp_massive vs. andere

Alternative	Wann stattdessen	Kompromiss
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

Mehr

Ressourcen

📖 Offizielle README auf GitHub lesen

🐙 Offene Issues ansehen

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