Demand Forecasting

Demand Forecasting

Forecasts demand per product (and optionally per region or channel) from sales history — daily, weekly or monthly — with a range for every number. It picks the best-fitting model by backtest, handles slow and intermittent movers (Croston/SBA), refuses to guess when history is too short, and hands lead-time demand to the Inventory Intelligence & Optimizer server for safety stock. Reads Excel, Tally and CSV exports with their own column names.

Registry name	ai.saifs/demand-forecasting
Endpoint	https://demand-forecaster-mcp.saifs.ai/mcp
Transport	Streamable HTTP (remote, nothing to install)
Auth	OAuth 2.1 — PKCE (S256) and dynamic client registration; your client opens a sign-in page on first use
Publisher	SAIFS AI

Install

VS Code / GitHub Copilot

Add to .vscode/mcp.json (workspace) or your user mcp.json:

{
  "servers": {
    "saifs-demand-forecasting": {
      "type": "http",
      "url": "https://demand-forecaster-mcp.saifs.ai/mcp"
    }
  }
}

Then start the server from the MCP view and sign in when prompted.

Claude (claude.ai, Desktop)

Settings → Connectors → Add custom connector, and paste:

https://demand-forecaster-mcp.saifs.ai/mcp

Claude Code

claude mcp add --transport http saifs-demand-forecasting https://demand-forecaster-mcp.saifs.ai/mcp

Any other MCP client

Point it at https://demand-forecaster-mcp.saifs.ai/mcp using the Streamable HTTP transport. The server returns 401 with a WWW-Authenticate: Bearer … resource_metadata=… challenge, so OAuth-aware clients sign in automatically.

Tools

Tool	What it does
demand_forecaster	Forecasts demand from sales rows (date + quantity is enough; SKU, region, channel and lead time optional) for up to about 200 products and 2,500 rows. Returns forecasts with prediction intervals, the chosen model and its backtest score, and lead-time demand for reordering.
demand_forecaster_get_engine	For larger sales files: returns a runnable Python script that produces the same forecasts locally, so the data never leaves the user's machine.

Example prompts

• “Here's our Tally sales export. How much of each item will we sell over the next 13 weeks?”
• “Which products are slow movers, and what should we expect from them next month?”
• “Forecast monthly demand for spare part SP-220 with a 95% range.”

Support

• Website: https://saifs.ai
• Issues: https://github.com/Saify-Technologies/saifs-ai-mcps-docs/issues
• Privacy: https://saifs.ai/privacy_policy