Narvar MCP for AI. Manage post-purchase logistics through conversation.
Works with every AI agent you already use
…and any MCP-compatible client








How this MCP server connects to your AI agent
Narvar MCP handles everything that happens after a customer clicks 'Buy.' Use it to manage returns, track shipments across any carrier, and calculate precise delivery dates—all without leaving your chat window.
It turns complex logistics coordination into simple conversations.
What AI agents can do with Narvar Automation
Get estimated delivery dates
Uses POST but acts as a query.
Calculate estimated delivery dates (EDD)
Create return
Initiate a return request for an order
Trigger notification
Trigger a transactional notification
Looks up live tracking information for any package using its carrier number.
Initiates a return request for an order and generates necessary shipping labels.
Retrieves all details about a specific purchase, including items purchased and fulfillment status.
Triggers transactional emails or SMS messages to notify customers of key shipment updates.
Determines the expected delivery date for an order, helping optimize checkout experiences.
Ask an AI about this
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What AI agents can do with Narvar: 5 Tools for E-commerce Flow
These five tools allow you to handle the entire life cycle of an online purchase, from calculating delivery dates to automatically processing returns.
Make your AI actually useful.
Add this MCP to Claude, Cursor, or Windsurf and your AI stops guessing. It gets real tools to look things up, take action, and handle the stuff you keep doing by hand.
Start using Narvar on VinkiusGet Estimated Delivery Dates
Uses POST but acts as a query. Calculate estimated delivery dates (EDD)
Create Return
Initiate a return request for an order
Trigger Notification
Trigger a transactional notification
Get Order
Get comprehensive order details
Get Tracking
Get real-time tracking information for a shipment
Security and governance baked right in.
Pick your AI client below to get set up. Just create a Vinkius account, subscribe, and you're instantly up and running. We handle the entire backend infrastructure, delivering out-of-the-box support for HTTPS Streamable, SSE, and OAuth2—zero messy routing required.
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Built on the Model Context Protocol (MCP) for Claude, ChatGPT, Cursor, and more
The Model Context Protocol standardizes how applications expose capabilities to LLMs. Instead of operating in isolation, your AI gains direct access to external platforms, live data, and real-world actions through secure, standardized connections.
This connection provides 5 powerful capabilities that interface natively with Claude, ChatGPT, Cursor, and other compatible AI platforms. No middleware. No custom integration required.
The chaos of post-purchase logistics, Solved with Vinkius AI Gateway
Right now, handling an order update means jumping through hoops. You pull up the internal dashboard for the order details, then open a separate tab to check the carrier's tracking site, and maybe you have to log into another system just to send an SMS notification about a delay. It’s copy-pasting numbers and switching contexts constantly.
With this MCP, all that friction disappears. Your agent pulls everything together conversationally. Instead of showing four different screens, the AI client gives one clear answer, whether it's pulling comprehensive order data using get_order or telling you exactly when to expect the package.
Getting shipment status with Narvar MCP
The manual process involves finding the tracking number, pasting it into a carrier portal, waiting for the page to load, and then reading out what you found. If there are multiple carriers, you repeat that whole sequence until you have all the answers.
Now, you simply ask your agent to get real-time tracking info using get_tracking. It handles the lookup across different carriers and spits the current status right back into the chat. That’s it.
What your AI can actually do with this
Managing post-purchase communication shouldn't feel like juggling five different dashboards. With this MCP, you can take full control of the customer journey from order confirmation through final delivery. Your agent becomes a dedicated logistics coordinator. Need to know where that package is? Just ask your AI client to get real-time status updates using get_tracking.
The system also handles returns; instead of emailing forms, you simply tell it to initiate a request and generate labels via create_return. For customer service reps, retrieving full order details—including line items and fulfillment status—is instant with get_order. You can even trigger automated alerts for customers using trigger_notification whenever a shipment hits a key milestone.
Plus, you don't have to guess arrival times; calculating precise delivery dates is simple with get_estimated_delivery_dates. Because this MCP lives on the Vinkius Marketplace, you connect once and immediately gain access to advanced e-commerce tools that used to require multiple integrations.
019ea5fb-384c-7335-9fe1-2ae81da807b3 Here's how it actually works
The bottom line is you get centralized logistics intelligence without switching between carrier websites or internal systems.
Subscribe to this MCP and provide your Narvar API key.
Your AI client sends a request—for example, asking about a specific shipment or order.
The tool executes the necessary lookup (like checking tracking info) and returns actionable data directly into your conversation.
Who is this actually for?
This MCP is for e-commerce operations. It helps the customer support agent who spends hours copying tracking numbers and navigating multiple carrier sites, and it assists the fulfillment manager tired of manual status checks.
Answers 'Where is my order?' queries instantly by calling get_tracking and processing returns using create_return without ever leaving the chat.
Monitors overall shipment health across multiple carriers, checks fulfillment status with get_order, and ensures customers get accurate delivery estimates.
Analyzes order flow data to predict potential delays or optimize checkout messaging using get_estimated_delivery_dates.
What Changes When You Connect
Instant Answers: Stop asking customers to wait while you check carrier portals. Your AI client instantly uses get_tracking to give them the exact status, reducing support time dramatically.
Streamlined Returns: Forget emailing forms or manually generating labels. Use create_return to handle the entire return workflow conversationally.
Better Checkout UX: You can use get_estimated_delivery_dates directly in your chat flow to answer 'When will it arrive?' before they even hit checkout, boosting conversion confidence.
Complete Visibility: Get a single view of everything. The get_order tool pulls all necessary details—items, status, etc.—so you never have to cross-reference multiple systems.
Proactive Customer Care: Instead of waiting for a complaint, use trigger_notification to automatically inform customers when their package leaves the warehouse or is out for delivery.
See it in action
A customer asks about a delayed order
The agent doesn't know the tracking number offhand. They ask the AI to check the shipment status using get_tracking, which immediately reports 'Delayed: New ETA is Sept 15th.' The agent then uses trigger_notification to send an automated apology alert, all in one chat.
Fulfilling a large bulk order
The ops manager needs confirmation that all parts are ready. They use get_order for the specific SKU group. The tool reports 'Fulfillment status: Ready to ship,' giving them the green light to proceed with packaging.
Handling a damaged item return
The customer complains about damage. The agent immediately uses create_return, which processes the return request and generates the required prepaid shipping label right in the chat thread for the customer to download.
Optimizing checkout messaging
A shopper is checking out but hesitates about timing. The system calls get_estimated_delivery_dates, which provides a precise date (e.g., 'Arrives Thursday between 2 PM and 5 PM'). This confidence boost helps push the sale through.
The honest tradeoffs
Treating it like a database query
Manually running five different reports (tracking, order details, returns) in separate internal dashboards to piece together one customer story.
Let your agent handle the heavy lifting. Use get_order and then chain that data with get_tracking to build a single narrative for the user. This keeps everything conversational.
Ignoring the post-purchase automation
Only responding when the customer complains about a delay, making the process reactive.
Use trigger_notification proactively. Set up automated alerts to send updates—like 'Out for Delivery'—before the customer even has to ask you.
Forgetting return steps
Asking a customer about a damaged item and just telling them to email us a form.
Use create_return. It handles the request initiation and generates the label in one step, making it frictionless for both you and the customer.
When It Fits, When It Doesn't
You should use this MCP if your team spends more time than necessary switching between carrier websites, internal CRM dashboards, or email threads to answer simple 'where is my order' questions. It excels at managing the full life cycle of a product after purchase—from calculating when it will arrive (get_estimated_delivery_dates) to handling the paperwork for returns (create_return). Don't use this if your primary goal is inventory forecasting or accounting ledger management; those require specialized ERP connections. This tool focuses strictly on customer-facing logistics and order status. If you only need to read static data, any simple API connector will work, but if you need the system to act—like sending a notification (trigger_notification) or calculating dynamic dates—this MCP is what you need.
Questions you might have
How does Narvar MCP handle multiple shipping carriers? +
The tool manages various carrier tracking numbers so you don't have to switch portals. You just provide the number, and get_tracking retrieves the status regardless of who is handling the delivery.
Can I use Narvar MCP to predict when a product will arrive? +
Yes, using get_estimated_delivery_dates allows you to calculate precise expected arrival dates. This data helps give customers confidence during checkout and in support chats.
What is the difference between get_order and get_tracking? +
get_order provides comprehensive details about the purchase itself—the items, who bought it, etc. get_tracking only focuses on the physical movement of the package using its tracking number.
Does Narvar MCP help with customer communication? +
Absolutely. You can use trigger_notification to automatically send status updates via Email or SMS when key events happen, so you don't have to manually write every message.
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