Massive

A collection of MCP servers and Claude skills for professional stock analysis. Built around a structured swing/long-term trading workflow that pulls live market data, technical indicators, financials, news, and chart visualizations — and turns them into actionable trade plans with explicit entry/SL/TP/R:R/sizing.

The repo contains:

• MCP servers that expose external data + chart APIs as tools
• Claude skills that orchestrate those tools into trade-grade analysis workflows

Designed for Claude Desktop and Claude Code.

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Repository layout

SwinGrade-MCPs/
├── src/massive_mcp/         ← MCP #1: massive-mcp (Massive.com Stocks REST API)
├── tests/                   ← tests for massive-mcp
├── pyproject.toml           ← massive-mcp package
│
├── chart-img-mcp/           ← MCP #2: chart-img-mcp (TradingView chart snapshots)
│   ├── src/chart_img_mcp/
│   ├── tests/
│   └── pyproject.toml
│
└── skills/                  ← Claude skills that drive the workflow
    ├── massive-trade-analysis/
    ├── market-macro-context-analysis/
    ├── earnings-calendar-check/
    ├── position-management/
    └── setup-edge-validation/

Layout note: massive-mcp is at the repo root (legacy from when the repo started as just that MCP), while chart-img-mcp lives in its own subdirectory. Future MCPs will follow the subdirectory pattern.

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MCP servers

massive-mcp — Massive.com Stocks REST API

Wraps Massive.com (Polygon-style). 31 tools across:

Group	Tools
Aggregates / bars	get_aggregates, get_previous_close, get_daily_market_summary, get_daily_ticker_summary
Quotes & trades	get_last_quote, get_last_trade, get_quotes, get_trades
Snapshots	get_snapshot, get_all_snapshots, get_top_movers
Tickers / reference	list_tickers, get_ticker_overview, get_related_tickers, list_ticker_types
Market reference	get_market_status, get_market_holidays, list_exchanges, list_condition_codes
News	get_news
Indicators	get_sma, get_ema, get_rsi, get_macd
Corporate actions	get_dividends, get_splits, get_ipos, get_ticker_events
Financials	get_financials, get_short_interest, get_short_volume

Install:

python -m venv .venv
.venv/Scripts/python -m pip install -e .   # Windows
# or .venv/bin/python -m pip install -e .   # mac/linux

Then in Claude Desktop's claude_desktop_config.json:

{
  "mcpServers": {
    "massive": {
      "command": "C:\\path\\to\\SwinGrade-MCPs\\.venv\\Scripts\\massive-mcp.exe",
      "env": {
        "MASSIVE_API_KEY": "your_key",
        "MASSIVE_AUTH_MODE": "bearer"
      }
    }
  }
}

chart-img-mcp — TradingView chart snapshots

Wraps chart-img.com. 5 tools: generate_chart, generate_mini_chart, generate_chart_to_storage, list_exchanges, find_symbol. Returns images inline in Claude Desktop, plus optional CDN URLs.

See chart-img-mcp/README.md for install + config.

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Claude skills

The skills live in skills/ and compose into a complete trading workflow:

                ┌─ market-macro-context-analysis  (regime score 0–10)
                │
massive-trade-analysis ─┼─ earnings-calendar-check  (pre-trade safety)
   (ENTRY plan)         │
                        ├─ setup-edge-validation    (empirical hit rates)
                        │
                        └─ chart-img: generate_chart (visual chart inline)
                              ↓
                    [trade is open]
                              ↓
                position-management        (TRIM / HOLD / EXIT)

Skill	Role
massive-trade-analysis	Entry plan: HQ-tag setup, ATR-based zone, cascaded TPs, R:R-sized position
market-macro-context-analysis	SPY/QQQ regime score (used by trade-analysis as macro overlay)
earnings-calendar-check	Estimates next earnings date, flags trade-window overlap
position-management	Mid-trade decisions: trim/hold/trail/time-stop using MAE/MFE + R-multiple
setup-edge-validation	Backtests HQ tag rules empirically — replaces gut-feel probabilities

Install: copy skills/* to ~/.claude/skills/. Full instructions in skills/README.md.

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End-to-end example

After installing both MCPs and all skills, ask Claude Desktop:

"Swing trade plan for NVDA, $50k account"

The massive-trade-analysis skill will:

1. Call mcp__massive__get_market_status and pull SPY/QQQ via market-macro-context-analysis → macro overlay
2. Pull NVDA bars + EMAs + RSI + MACD + news + ticker overview
3. Compute HQ tag, ATR, support/resistance, R:R via compute_rr.py
4. (Optional) call earnings-calendar-check to flag earnings overlap
5. (Optional) call setup-edge-validation for empirical scenario probabilities
6. Render the trade-plan markdown
7. Call mcp__chart-img__generate_chart to attach a daily candle chart with EMA10/20/50/200 + RSI + MACD inline

You get a structured plan and the actual chart, side by side.

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Prerequisites

• Python 3.10+ on PATH
• API keys:
  • Massive.com: https://massive.com/docs
  • chart-img.com: https://chart-img.com/dashboard

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Why a single repo?

These pieces are designed to compose. Splitting them across repos forces users to clone N projects to get one workflow. Keeping them together means one git clone gets you the full toolchain — and the skills can reference the MCPs directly because they're co-located.

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License

MIT — see individual MCP pyproject.toml files.