# Demand Forecasting MCP server

> Forecasts product demand from historical sales data using various statistical models and provides prediction intervals.

- Repository: https://github.com/Saify-Technologies/saifs-ai-mcps-docs
- Page: https://hostedmcp.org/servers/saify-technologies/demand-forecasting
- Transport: remote
- Stars: 4
- License: MIT
- Latest version: 1.0.1

## Install

### Claude Code

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

### Cursor (`~/.cursor/mcp.json`)

```json
{
  "mcpServers": {
    "demand-forecasting": {
      "url": "https://demand-forecaster-mcp.saifs.ai/mcp"
    }
  }
}
```

### VS Code (`.vscode/mcp.json`)

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

### VS Code CLI

```sh
code --add-mcp '{"name":"demand-forecasting","type":"http","url":"https://demand-forecaster-mcp.saifs.ai/mcp"}'
```

### Codex

```sh
codex mcp add demand-forecasting --url https://demand-forecaster-mcp.saifs.ai/mcp
```

### Codex config.toml (`~/.codex/config.toml`)

```toml
[mcp_servers.demand-forecasting]
url = "https://demand-forecaster-mcp.saifs.ai/mcp"
```

### Gemini CLI

```sh
gemini mcp add -s user --transport http demand-forecasting https://demand-forecaster-mcp.saifs.ai/mcp
```

### Gemini settings.json (`~/.gemini/settings.json`)

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

## Tools (2)

- `demand_forecaster`
- `demand_forecaster_get_engine`

## FAQ

### What data formats does the server support?

The server can read Excel, Tally, and CSV exports, even with custom column names.

### How can I handle large sales files without uploading data?

You can use the demand_forecaster_get_engine tool to receive a runnable Python script that generates forecasts locally on your machine.

### What information is included in the forecast results?

The tool returns forecasts with prediction intervals, the selected model, its backtest score, and lead-time demand for reordering.