shopify-forecast-mcp

⚠️ v0.1.0 Alpha — Early release. API surface may change before v0.2. Feedback welcome: open an issue.

Merchant-native MCP server that connects Google's TimesFM 2.5 time-series foundation model to your Shopify store — so your AI assistant can answer "what does next month look like?" with a real forecast grounded in your order history.

No dashboards, no exports, no per-store training. Works with Claude Desktop, Claude Code, Cursor, and any MCP-compatible AI client.

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Why

Shopify has four official MCP servers, all buyer-facing or developer-facing. None serve merchant operations: forecasting, demand planning, promo analysis, anomaly detection.

Existing third-party tools either use weak models (moving averages, Prophet) or lock insights inside closed SaaS dashboards. This one:

• Runs TimesFM 2.5 (Google's 200M-param foundation model) — state of the art on the GIFT-Eval retail benchmark
• Pulls directly from Shopify Admin GraphQL with bulk operations, refund-aware normalization, multi-currency, and cost-based rate limiting
• Returns markdown tables with confidence bands that render natively in your MCP client
• Ships as a single uvx command — zero manual Python setup
• Is MIT licensed, free forever

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Quick start

Three steps to a working forecast in under 5 minutes:

1. Install uv (once)

# macOS / Linux
curl -LsSf https://astral.sh/uv/install.sh | sh

# Windows (PowerShell)
irm https://astral.sh/uv/install.ps1 | iex

2. Get a Shopify Admin API access token

Follow docs/SETUP.md to create a custom app, enable the required scopes (read_orders, read_all_orders, read_products, read_inventory), and generate an access token.

3. Add to Claude Desktop

Edit ~/Library/Application Support/Claude/claude_desktop_config.json (macOS) or %APPDATA%\Claude\claude_desktop_config.json (Windows) and add:

{
  "mcpServers": {
    "shopify-forecast": {
      "command": "uvx",
      "args": ["shopify-forecast-mcp"],
      "env": {
        "SHOPIFY_FORECAST_SHOP": "mystore.myshopify.com",
        "SHOPIFY_FORECAST_ACCESS_TOKEN": "shpat_xxxxxxxxxxxxxxxxxxxxxxxx"
      }
    }
  }
}

Restart Claude Desktop, then ask: "What does next month look like?"

First run downloads TimesFM 2.5 weights (~400MB, one-time). Subsequent forecasts are <10 seconds.

Prefer the terminal first? You can verify your setup before wiring Claude Desktop by running uvx shopify-forecast-mcp directly — it launches the MCP server on stdio and will exit cleanly on Ctrl-C when no client is attached. Or exercise the CLI: uvx --from shopify-forecast-mcp shopify-forecast revenue --horizon 7.

Alpha pre-release: if installing v0.1.0-rc1, use "args": ["--prerelease=allow", "shopify-forecast-mcp@0.1.0rc1"] instead.

See also: docs/SETUP.md for Claude Code + generic MCP client setup, Docker install, and multi-store configuration.

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Examples

Drop these into your AI client after setup:

Revenue forecasting — "What does next month look like?" Returns a daily-granularity revenue forecast for the next 30 days with an 80% confidence band (q10–q90).

Demand + reorder alerts — "Which SKUs need to be reordered in the next 2 weeks?" Returns top-N SKUs with projected demand vs current inventory, flagging stockout risk.

Promo analysis — "How did Black Friday perform vs last year?" Returns revenue lift, order lift, AOV change, discount depth, and post-promo hangover estimate for both windows side by side.

Scenario planning — "Compare 3 promo scenarios for December: 10% off, 20% off + free shipping, and BOGO." Returns 3 differentiated forecasts in one markdown response with per-scenario revenue, units, and margin implications.

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Tools

Seven MCP tools, full reference in docs/TOOLS.md:

Tool	Purpose
forecast_revenue	Store-level revenue forecast with confidence bands
forecast_demand	Product/collection/SKU demand + reorder alerts
analyze_promotion	Past promo vs baseline — lift, AOV, hangover
detect_anomalies	Flag days outside forecast quantile bands
compare_periods	Year-over-year / month-over-month comparison
compare_scenarios	What-if forecasting across 2-4 scenarios
get_seasonality	Explain learned seasonal patterns

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Architecture

Two-layer design: a pure-Python core library (Shopify client, time-series shaping, TimesFM forecaster, analytics, covariates) wrapped by a thin MCP server and a matching CLI. Core is importable and testable without the MCP runtime.

Dual-backend Shopify access: DirectBackend (httpx + access token, used in Docker and when SHOPIFY_FORECAST_ACCESS_TOKEN is set) or CliBackend (shopify store execute — browser OAuth, no token required, host-only).

Full diagrams + design decisions in docs/ARCHITECTURE.md.

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Configuration

Minimum required env vars:

Variable	Purpose
SHOPIFY_FORECAST_SHOP	Your store domain (e.g. mystore.myshopify.com)
SHOPIFY_FORECAST_ACCESS_TOKEN	Admin API access token (from custom app)

See docs/SETUP.md for the full env var table, multi-store config, Docker env passing, and optional tuning knobs.

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CLI

A standalone shopify-forecast CLI wraps the same core library without the MCP runtime — useful for scripting, cron, and CI:

uvx --from shopify-forecast-mcp shopify-forecast revenue --horizon 30
uvx --from shopify-forecast-mcp shopify-forecast demand --group-by product --top-n 10
uvx --from shopify-forecast-mcp shopify-forecast promo --start 2025-11-24 --end 2025-11-30
uvx --from shopify-forecast-mcp shopify-forecast compare --period-a 2024-11:2024-12 --period-b 2025-11:2025-12

Add --json to any verb for machine-readable output. See docs/SETUP.md#cli-usage for the full CLI reference.

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Docker

Run the MCP server (or any CLI verb) without installing Python:

docker run --rm -i \
  -e SHOPIFY_FORECAST_SHOP=mystore.myshopify.com \
  -e SHOPIFY_FORECAST_ACCESS_TOKEN=shpat_xxx \
  ghcr.io/mcostigliola321/shopify-forecast-mcp:latest

Two image variants:

• :latest — lazy model download on first call (smaller image, ~1.5GB)
• :bundled — TimesFM weights baked in (larger image, ~2.5GB, offline-capable)

Browser-based OAuth does NOT work in containers; Docker mode requires the access-token env var. See docs/SETUP.md#docker.

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Roadmap

v0.1.0 is the first public alpha, covering the full MVP (7 MCP tools + 4 CLI verbs + dual-backend). Future: see .planning/ROADMAP.md.

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Contributing

Feedback and bug reports welcome at GitHub Issues. For code contributions, see docs/ARCHITECTURE.md for the two-layer design and open a draft PR early.

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License

MIT. TimesFM 2.5 weights are Apache 2.0 (compatible).