mcp_massive MCP — Use Cases, Install & Live Demo

Why use it

Key features

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

What it looks like in practice

massive.replay ▶ ready

0/0

Install

Pick your 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
    }
  }
}

Open Claude Desktop → Settings → Developer → Edit Config. Restart after saving.

~/.cursor/mcp.json · .cursor/mcp.json

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

Cursor uses the same mcpServers schema as Claude Desktop. Project config wins over global.

VS Code → Cline → MCP Servers → Edit

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

Click the MCP Servers icon in the Cline sidebar, then "Edit Configuration".

~/.codeium/windsurf/mcp_config.json

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

Same shape as Claude Desktop. Restart Windsurf to pick up changes.

~/.continue/config.json

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

Continue uses an array of server objects rather than a map.

~/.config/zed/settings.json

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

Add to context_servers. Zed hot-reloads on save.

claude mcp add massive -- uvx mcp_massive

One-liner. Verify with claude mcp list. Remove with claude mcp remove.

Use Cases

Real-world ways to use mcp_massive

Analyze an options chain and price theoretical value

👤 Options traders, quant hobbyists ⏱ ~20 min advanced

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

Prerequisites

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

Flow

Find the right endpoint

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

→ Best-match endpoint + params
Pull chain

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

→ 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.✓ Copied

→ Table of strikes with BS-vs-market

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

Pitfalls

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

When to use: Year-end — how did my 10-position portfolio perform vs SPY?

Flow

Fetch prices

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

→ Price series per ticker
Compute weighted return

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

→ Portfolio YTD vs SPY YTD

Outcome: Quick portfolio report without spreadsheets.

Pitfalls

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

Combine with: filesystem

Screen stocks on fundamental criteria

👤 Value investors ⏱ ~20 min intermediate

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

Flow

Find endpoints

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

→ Endpoint choice
Screen

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

→ Matching tickers with key ratios

Outcome: A shortlist to research further.

Pitfalls

Fundamentals data lags quarterly filings by weeks — Check as_of dates

Combinations

Pair with other MCPs for X10 leverage

massive + filesystem

Persist analysis to CSV for Excel follow-up

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

Tools

What this MCP exposes

Tool	Inputs	When to call	Cost
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

Cost & Limits

What this costs to run

API quota: Per Massive.com plan
Tokens per call: Raw series can be large — use DataFrame + SQL to slice
Monetary: Free tier exists; paid for real-time / higher volume
Tip: store_as + query_data is dramatically cheaper than re-fetching and re-parsing each time.

Security

Permissions, secrets, blast radius

Credential storage: MASSIVE_API_KEY env var

Data egress: 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.

Troubleshooting

Common errors and fixes

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.

Alternatives

mcp_massive vs others

Alternative	When to use it instead	Tradeoff
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

More

Resources

📖 Read the official README on GitHub

🐙 Browse open issues

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