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.
| 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