Demand Forecast Calculator Connector for AI agents.
3 live capabilities
Predict future inventory needs with data-backed supply chain forecasting.
Waiting for input…
Why people use Demand Forecast Calculator
Demand Forecast Calculator for Accurate Supply Chain Planning
This Connector changes that by letting your AI client run the heavy math for you. You provide the historical data, and it instantly runs multiple forecasting models, providing you with the exact Mean Absolute Error for each. You get a clear choice of which model is actually performing best, turning a multi-hour manual task into a few seconds of analysis.
What Vinkius changes
You get a data-backed demand forecast with a clear picture of each model's historical accuracy.
Use it from Claude, ChatGPT, Cursor or another AI client you already have.
One account · 5,900+ Connectors
- Real-world use case 01
Validating a model for a new product
A manager sees erratic sales for a new item.
- Real-world use case 02
Justifying an inventory order
A warehouse head needs to justify a new order.
- Real-world use case 03
Comparing forecast accuracy
A planner wants to know which model to trust.
Complete set · 3capabilities
The complete Demand Forecast Calculator capability set.
These are the exact actions your AI can choose when you ask it to work with Demand Forecast Calculator.
01—03
3 capabilities in this set.
Part of 3 available through Demand Forecast Calculator.
- 01 Capability
Analyze exponential smoothing
Calculates demand forecasts using Exponential Smoothing to account for recent trends. It helps you see how demand momentum shifts over time.
- 02 Capability
Analyze sma
Generates a forecast based on the Simple Moving Average of your historical data points. This provides a steady, balanced baseline.
- 03 Capability
Analyze wma
Produces a Weighted Moving Average forecast where you can prioritize specific past periods. It's great for reacting to recent spikes.
Set up in minutes
One URL. Then ask Demand Forecast Calculator to work.
Claude and ChatGPT only need the Connector URL. Copy it once, add it in settings, and use Demand Forecast Calculator from the conversation.
Choose your client
Live previewAdvanced clients IDE · CLI
Claude · Web + desktop
Connector URL · ready to paste
Streamable HTTPhttps://edge.vinkius.com/vk_preview_sv6aatMHqHyqOR4XjXsLusL61ZUZceGD44vNHlLt/mcp - Step 01
Open Connectors
In Claude Web or Claude Desktop, open Settings and choose Connectors.
- Step 02
Add the URL
Choose Add custom connector, name it Demand Forecast Calculator, and paste the URL above.
- Step 03
Turn it on in chat
Select +, open Connectors, and enable Demand Forecast Calculator for the conversation.
ChatGPT · Web + desktop
Connector URL · ready to paste
Streamable HTTPhttps://edge.vinkius.com/vk_preview_sv6aatMHqHyqOR4XjXsLusL61ZUZceGD44vNHlLt/mcp - Step 01
Open MCP settings
On desktop, open Settings and MCP servers. On web, open your workspace app or connector settings.
- Step 02
Add the URL
Choose Add server with Streamable HTTP, or create a custom MCP app, then paste the Demand Forecast Calculator URL.
- Step 03
Save and start
Save the connection and enable Demand Forecast Calculator in your conversation. Desktop may ask you to restart once.
Cursor · IDE configuration
Advanced setup
{
"mcpServers": {
"demand-forecast-calculator": {
"url": "https://edge.vinkius.com/vk_preview_sv6aatMHqHyqOR4XjXsLusL61ZUZceGD44vNHlLt/mcp"
}
}
}- Step 01
Open MCP Settings
Press Cmd+Shift+P (macOS) or Ctrl+Shift+P (Windows/Linux) → search "MCP Settings"
- Step 02
Add the server config
Paste the JSON configuration above into the mcp.json file that opens
- Step 03
Save the file
Cursor will automatically detect the new Connector
- Step 04
Start using Demand Forecast Calculator
Open Agent mode in chat and ask: "Using Demand Forecast Calculator, help me...". 3 tools available
VS Code Copilot · IDE configuration
Advanced setup
{
"mcpServers": {
"demand-forecast-calculator": {
"url": "https://edge.vinkius.com/vk_preview_sv6aatMHqHyqOR4XjXsLusL61ZUZceGD44vNHlLt/mcp"
}
}
}- Step 01
Create MCP config
Create a .vscode/mcp.json file in your project root
- Step 02
Add the server config
Paste the JSON configuration above
- Step 03
Enable Agent mode
Open GitHub Copilot Chat and switch to Agent mode using the dropdown
- Step 04
Start using Demand Forecast Calculator
Ask Copilot: "Using Demand Forecast Calculator, help me...". 3 tools available
Windsurf · IDE configuration
Advanced setup
{
"mcpServers": {
"demand-forecast-calculator": {
"url": "https://edge.vinkius.com/vk_preview_sv6aatMHqHyqOR4XjXsLusL61ZUZceGD44vNHlLt/mcp"
}
}
}- Step 01
Open MCP Settings
Go to Settings → MCP Configuration or press Cmd+Shift+P and search "MCP"
- Step 02
Add the server
Paste the JSON configuration above into mcp_config.json
- Step 03
Save and reload
Windsurf will detect the new server automatically
- Step 04
Start using Demand Forecast Calculator
Open Cascade and ask: "Using Demand Forecast Calculator, help me...". 3 tools available
Cline · IDE configuration
Advanced setup
{
"mcpServers": {
"demand-forecast-calculator": {
"url": "https://edge.vinkius.com/vk_preview_sv6aatMHqHyqOR4XjXsLusL61ZUZceGD44vNHlLt/mcp"
}
}
}- Step 01
Open Cline MCP Settings
Click the Connectors icon in the Cline sidebar panel
- Step 02
Add remote server
Click "Add Connector" and paste the configuration above
- Step 03
Enable the server
Toggle the server switch to ON
- Step 04
Start using Demand Forecast Calculator
Ask Cline: "Using Demand Forecast Calculator, help me...". 3 tools available
Claude Code · Terminal command
Advanced setup
claude mcp add demand-forecast-calculator --transport http "https://edge.vinkius.com/vk_preview_sv6aatMHqHyqOR4XjXsLusL61ZUZceGD44vNHlLt/mcp"- Step 01
Install Claude Code
Run npm install -g @anthropic-ai/claude-code if not already installed
- Step 02
Add the Connector
Run the command above in your terminal
- Step 03
Verify the connection
Run claude mcp to list connected servers, or type /mcp inside a session
- Step 04
Start using Demand Forecast Calculator
Ask Claude: "Using Demand Forecast Calculator, show me...". 3 tools are ready
Where the request belongs
Work Demand Forecast Calculator can move forward.
Supply chain managers who are tired of manual spreadsheets and operations leads who need to justify inventory spend to their bosses.
Supply Chain Planner
Uses this to justify stock levels to stakeholders during weekly planning meetings.
Inventory Manager
Uses this to identify which products are trending and need more warehouse space.
Operations Analyst
Uses this to automate the backtesting phase of demand modeling to save hours of manual math.
Build the capability set
Add more capabilities.
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Bring your own AI
Change the model, client or framework. Keep Demand Forecast Calculator connected.
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Before you connect
Questions about Demand Forecast Calculator.
The practical details behind the request, access and result.
How does the Demand Forecast Calculator help with inventory?
It gives you a 3-month projection of what you'll likely sell, helping you decide how much stock to order so you don't run out or overbuy.
Can I see how accurate the forecasts are?
Yes, the Connector calculates Mean Absolute Error and Mean Absolute Percentage Error for each model so you can see which one fits your history best.
What is the difference between the SMA and WMA options?
Simple Moving Average treats all past months equally, while Weighted Moving Average lets you prioritize recent months over older ones.
Does the Demand Forecast Calculator handle my historical data?
You provide the historical demand numbers, and the Connector handles all the mathematical modeling and error backtesting.
How do I know which forecasting model to choose?
You can run all three methods and compare the MAPE scores. The model with the lowest percentage error is usually your most reliable bet for that specific product.
Is this useful for seasonal products?
It's great for identifying trends. For products with very complex yearly cycles, you'll want to use the Exponential Smoothing option to help capture recent momentum.
What forecasting methods are supported?
The server supports Simple Moving Average (analyze_sma), Weighted Moving Average (analyze_wma), and Exponential Smoothing (analyze_exponential_smoothing).
How is the accuracy of the forecast measured?
Accuracy is measured using Mean Absolute Error (MAE) and Mean Absolute Percentage Error (MAPE) through a backtesting process on your historical data.
What inputs are required for the SMA capability?
The analyze_sma capability requires an array of historical demand values and a window size representing the number of periods to include in the average.
One connection away
Give your agent a direct line to Demand Forecast Calculator.
Connect Demand Forecast Calculator once. Keep it beside 5,900+ managed Connectors when the next task needs more.
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