Loyalty Points Engine Connector for AI agents.
3 live capabilities
Maximize your rewards by calculating the best time to redeem points for maximum value.
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Why people use Loyalty Points Engine
Loyalty Points Value Engine for Smarter Reward Optimization
This Connector changes that by doing the math for you. You just tell your agent what you're thinking about, and it runs the numbers to show you the real dollar value. You get a clear answer on whether you're getting a good deal or if you're better off waiting for a bigger win.
What Vinkius changes
You get a data-driven answer on whether to spend your points now or keep saving them.
Use it from Claude, ChatGPT, Cursor or another AI client you already have.
One account · 6,100+ Connectors
- Real-world use case 01
Travel Planning
A user has 50k points and wants to know if a 4-star hotel now is better than a 5-star hotel later.
- Real-world use case 02
Retail Optimization
A shopper wants to know if a $100 gift card is better than a $150 gift card that requires 20% more points.
- Real-world use case 03
Budgeting
A user wants to see how much more they need to spend to reach a 'Gold' status reward.
Complete set · 3capabilities
The complete Loyalty Points Engine capability set.
These are the exact actions your AI can choose when you ask it to work with Loyalty Points Engine.
01—03
3 capabilities in this set.
Part of 3 available through Loyalty Points Engine.
- 01 Capability
Evaluate current efficiency
Calculates the points-to-dollar ratio for every redemption option you're considering. This helps you see the real cash value of your rewards.
- 02 Capability
Analyze redemption strategy
Gives a final recommendation on whether to spend now or wait based on your earning speed. It takes your current habits into account to find the best path forward.
- 03 Capability
Calculate upgrade implication
Shows the exact spend required to reach a target reward tier. It removes the guesswork from your long-term loyalty goals.
Set up in minutes
One URL. Then ask Loyalty Points Engine to work.
Claude and ChatGPT only need the Connector URL. Copy it once, add it in settings, and use Loyalty Points Engine 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_6PX7ljOBdvPuY6ZuVcuxIHu1wUcoJ9X9RQd5BT6v/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 Loyalty Points Engine, and paste the URL above.
- Step 03
Turn it on in chat
Select +, open Connectors, and enable Loyalty Points Engine for the conversation.
ChatGPT · Web + desktop
Connector URL · ready to paste
Streamable HTTPhttps://edge.vinkius.com/vk_preview_6PX7ljOBdvPuY6ZuVcuxIHu1wUcoJ9X9RQd5BT6v/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 Loyalty Points Engine URL.
- Step 03
Save and start
Save the connection and enable Loyalty Points Engine in your conversation. Desktop may ask you to restart once.
Cursor · IDE configuration
Advanced setup
{
"mcpServers": {
"loyalty-points-value-engine": {
"url": "https://edge.vinkius.com/vk_preview_6PX7ljOBdvPuY6ZuVcuxIHu1wUcoJ9X9RQd5BT6v/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 Loyalty Points Engine
Open Agent mode in chat and ask: "Using Loyalty Points Engine, help me...". 3 tools available
VS Code Copilot · IDE configuration
Advanced setup
{
"mcpServers": {
"loyalty-points-value-engine": {
"url": "https://edge.vinkius.com/vk_preview_6PX7ljOBdvPuY6ZuVcuxIHu1wUcoJ9X9RQd5BT6v/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 Loyalty Points Engine
Ask Copilot: "Using Loyalty Points Engine, help me...". 3 tools available
Windsurf · IDE configuration
Advanced setup
{
"mcpServers": {
"loyalty-points-value-engine": {
"url": "https://edge.vinkius.com/vk_preview_6PX7ljOBdvPuY6ZuVcuxIHu1wUcoJ9X9RQd5BT6v/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 Loyalty Points Engine
Open Cascade and ask: "Using Loyalty Points Engine, help me...". 3 tools available
Cline · IDE configuration
Advanced setup
{
"mcpServers": {
"loyalty-points-value-engine": {
"url": "https://edge.vinkius.com/vk_preview_6PX7ljOBdvPuY6ZuVcuxIHu1wUcoJ9X9RQd5BT6v/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 Loyalty Points Engine
Ask Cline: "Using Loyalty Points Engine, help me...". 3 tools available
Claude Code · Terminal command
Advanced setup
claude mcp add loyalty-points-value-engine --transport http "https://edge.vinkius.com/vk_preview_6PX7ljOBdvPuY6ZuVcuxIHu1wUcoJ9X9RQd5BT6v/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 Loyalty Points Engine
Ask Claude: "Using Loyalty Points Engine, show me...". 3 tools are ready
Where the request belongs
Work Loyalty Points Engine can move forward.
For the 'travel hacker' who wants to maximize every cent, the savvy shopper who hates wasting rewards, and the finance enthusiast who treats point optimization like a game. It's for anyone tired of doing mental math to see if their loyalty points are actually worth the spend.
Travel Hacker
Plans out multi-city trips and wants to know if a points-based flight is actually a good deal compared to cash.
Reward Program Manager
Analyzes loyalty data to ensure the company's redemption tiers are balanced and profitable.
Savvy Shopper
Compares different store rewards to find the highest 'cash back' equivalent for their points.
Build the capability set
Add more capabilities.
Each Connector adds new actions and data without changing how you work.
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Coffee & Lunch Habit Cost Calculator
Quantify the long-term opportunity cost of daily habits like coffee or lunch.
All Digital Rewards
Incentive and reward orchestration. manage participants, points, and catalogs via AI.
AwardWallet
Manage loyalty points, travel rewards, and itineraries via AwardWallet. monitor your miles and trips directly via AI.
Bring your own AI
Change the model, client or framework. Keep Loyalty Points Engine connected.
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Claude -
ChatGPT -
Gemini -
Cursor -
VS Code -
Windsurf -
ZCode -
Cline -
Zed -
Continue -
Kiro -
Roo Code -
Zencoder -
Goose -
Void -
Augment Code -
Amp -
Qodo -
Tabnine -
Pieces -
Sourcegraph Cody -
JetBrains -
Warp -
Amazon Q -
Antigravity -
BoltAI -
Raycast -
Jan -
LM Studio -
AnythingLLM -
Open WebUI -
Msty -
Cherry Studio -
LibreChat -
TypingMind -
Chorus -
5ire -
n8n -
LangChain -
LlamaIndex -
CrewAI -
Vercel AI SDK
Before you connect
Questions about Loyalty Points Engine.
The practical details behind the request, access and result.
Can the Loyalty Points Value Engine help me decide when to spend my points?
Yes, it compares your current options against future goals to give a clear spend or save recommendation. It helps you see which redemption offers the best actual value for your points.
How does the Loyalty Points Value Engine calculate reward value?
It looks at the points-to-dollar ratio of different rewards to show you which one gives you the most 'bang for your buck.' This helps you identify the highest-value redemptions.
Can I use the Loyalty Points Value Engine to see how much I need to spend for a tier upgrade?
Yes, you can see the exact spend required to reach a specific loyalty tier based on your current balance. It takes your specific earning rate into account to give you a precise target.
Is the Loyalty Points Value Engine good for travel rewards?
It's perfect for travel rewards, helping you see if a flight or hotel booking is a good use of your points right now. It helps you weigh immediate rewards against higher-tier travel goals.
Does the Loyalty Points Value Engine factor in my spending habits?
Yes, it can factor in your earning rate so it knows how quickly you'll reach your next goal. This allows the agent to provide more accurate timelines for your reward milestones.
Can I use the Loyalty Points Value Engine to compare two different rewards?
Yes, you can input different point requirements and cash values to see which one is more efficient. It provides a clear side-by-side comparison of the points-to-dollar ratios.
How does the engine determine if I should wait?
The engine compares the extra cash value gained from a higher tier against the required_additional_spend calculated by calculate_upgrade_implication. If the gain exceeds the cost, it recommends waiting.
What inputs are needed for a strategy analysis?
You need your current points_balance, the indices of your current and target options, the full list of redemption_options as a JSON array, and your points_earned_per_dollar_spent rate.
Can I use this for multiple different loyalty programs?
Yes. Simply provide the specific redemption options and earning rates for any program you are analyzing.
One connection away
Give your agent a direct line to Loyalty Points Engine.
Connect Loyalty Points Engine once. Keep it beside 6,100+ managed Connectors when the next task needs more.
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