Supercharge your AI with Constructor. Audit and debug e-commerce discovery flows.
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Constructor MCP lets your AI agent manage product discovery end-to-end. Instead of manually testing site search or navigating complex category dashboards, you ask for it conversationally.
It handles everything from ML-ranked searching to applying strict filters (size, color) and generating personalized recommendations. You get a full audit trail of how products are found online—all through chat.
What your AI can do
Autocomplete
Predicts available search terms as you type a query into the agent.
Browse brand
Retrieves products by inspecting deep arrays associated with a specific brand.
Browse category
Lists all available products within an entire product department or category hierarchy.
Run general product searches using ML ranking based on keywords and user intent.
Refine search results immediately, restricting the list to specific colors, sizes, or features.
Navigate deep product directory trees and see all available products within a defined department.
Pull dynamic recommendations using collaborative filtering models based on simulated user activity.
Inspect deep arrays of products belonging to a specific manufacturer or brand line.
Retrieve predefined marketing clusters and static product groups for promotional review.
Ask an AI about this
Compatible AI Apps
OAuth 2.0 Compatible Gemini Waiting for input…
Constructor: 10 Product Discovery Tools
These ten tools give your agent full control over every aspect of product discovery on your site, from basic searching to complex filtering.
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 Constructor on VinkiusAutocomplete
Predicts available search terms as you type a query into the agent.
Browse Brand
Retrieves products by inspecting deep arrays associated with a specific brand.
Browse Category
Lists all available products within an entire product department or category...
Browse Collection
Identifies and retrieves pre-set groups of marketing items or static collections.
Custom Search
Executes a generalized search across the product catalog based on your input.
Search Filtered
Runs a search and restricts the results list to match specific attributes, like size or color.
Search Pagination
Checks how search results behave across multiple pages of output.
Get Recommendations
Pulls personalized suggestions for products using defined filtering models.
Search Products
Finds products by accessing the core record set within the platform.
Search Sorted
Lists product results based on structured rules, such as best sellers or price order.
Connect to your AI in seconds. 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.
Claude AI
Open Claude Settings
Go to claude.ai, click your profile icon, then navigate to Customize → Connectors.
Add Custom Connector
Click the "+" button and select Add custom connector. Paste your Vinkius endpoint URL:
https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp
Replace [YOUR_TOKEN_HERE] with your token from
cloud.vinkius.com. For OAuth-protected servers, expand Advanced
settings to add credentials.
Start a conversation
Open a new chat. The Constructor integration is available immediately — no restart needed.
Choose How to Get Started
Build a custom MCP for your own tools, or connect a ready-made integration from our catalog.
Build Your Own
Turn any API into an MCP. Import a spec, define Agent Skills, or deploy with MCPFusion.
- Import from OpenAPI, Swagger, or YAML specs
- Create Agent Skills with progressive disclosure
- Deploy to edge with MCPFusion framework
- Built in DLP, auth, and compliance on every call
- Real time usage dashboard and cost metering
- Publish to catalog or keep private
Make Your AI Do More
Start with Constructor, then connect any of our 5,000+ other servers whenever your AI needs more. One click, no limits.
- Use this MCP plus 5,000+ others, all in one place
- Add new capabilities to your AI anytime you want
- Every connection is secured and compliant automatically
- Track usage and costs across all your servers
- Works with Claude, ChatGPT, Cursor, and more
- New servers added to the catalog every week
Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by Constructor. All third-party trademarks, logos, and brand names are the property of their respective owners. Their use on this website is strictly for informational purposes to identify service compatibility and interoperability.
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Works with 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 10 powerful capabilities that interface natively with Claude, ChatGPT, Cursor, and other compatible AI platforms. No middleware. No custom integration required.
The Old Way: Dashboard Diving
Today, testing a simple change in your site's product logic means logging into multiple backend dashboards. You click through category trees; you manually apply filters to check combinations of colors and sizes; you copy data from one tab just to paste it into another for verification. It’s slow, tedious, and every test requires dedicated time that could be spent elsewhere.
With this MCP, you talk to your agent instead. You tell it the exact scenario—'Check all patio furniture in size 36.' The tool runs the complex query instantly, providing structured data results right there in the chat. It takes manual clicks and hours of investigation down to a single conversation prompt.
Discovering Products with Constructor
The biggest time-saver is eliminating the need for separate audit reports. You don't have to run five different API calls and stitch the results together yourself. The agent handles the orchestration, gathering product lists via `browse_category`, validating brands using `browse_brand`, and summarizing everything in one go.
Now, you treat your entire e-commerce platform like a conversational interface. Your ability to debug complex user flows moves from requiring specialized technical skills to simply asking the right question.
What your AI can actually do with this
You can take complete control of your site search experience using this MCP. Stop running manual tests on backend dashboards just to verify if product discovery works correctly. With this connection, you tell your agent what you need—like 'Show me all the waterproof jackets in size large' or 'What should we recommend for a first-time buyer?' The tool handles the complex queries and returns structured data instantly.
You can audit everything: check how deep a category tree goes, see exactly which brands are prioritized, or get recommendations based on simulated user behavior. Since this MCP lives within the Vinkius catalog, you connect your agent once and gain access to all these critical e-commerce tools without switching platforms.
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Here's how it actually works
The bottom line is you can audit and test all of your site's product discovery logic without ever leaving the conversation window.
Subscribe to this MCP and enter your Constructor.io Public API Key into the Vinkius platform.
Your AI agent connects to the toolset, allowing it to execute complex e-commerce queries through natural language prompts.
You receive structured data containing product listings, filtered results, or recommended items directly in your chat interface.
Who is this actually for?
This MCP is essential for E-commerce Managers, Product Owners, and Developers. If your job involves verifying search ranking, debugging category logic, or auditing recommendation performance, this tool saves hours of manual dashboard testing.
Verifies search rankings and checks if curated collections are properly mapped before a campaign launch.
Monitors how category browsing performs and confirms that attribute filtering logic works exactly as designed for new product lines.
Tests and debugs search API parameters, simulating user journeys across different product directories using natural language prompts.
What Changes When You Connect
Stop wasting time manually checking search results. Use search_filtered to instantly test how product lists behave when you apply strict rules like size or color.
Never guess what a customer will look for again. The agent uses autocomplete to predict popular terms as you type, making your debugging faster.
Audit marketing efforts easily. Instead of checking dashboard reports, use browse_collection to verify that every curated product cluster is correctly listed and rankable.
Improve recommendation accuracy instantly. Run the get_recommendations tool to simulate user behavior and see exactly which products should be suggested on a given page.
Verify site structure without limits. Use browse_category to map out entire departmental trees, ensuring your product taxonomy is deep and functional for every client type.
See it in action
Debugging Size Availability
A QA tester needs to confirm that only red, size medium items show up. They run the agent and instruct it to 'Search for blue jackets' but add a filter using search_filtered for color: red and size: M. The system immediately verifies the correct subset of products.
Auditing Brand Visibility
An E-commerce Manager wants to ensure that all items from 'Brand X' show up when a user is browsing outdoor gear. They use browse_brand for 'Brand X' within the 'Outdoor Gear' category, confirming every product ID is active and visible.
Checking Pagination Logic
A Product Owner suspects that search results break down on page three. Instead of clicking through manually, they run search_pagination to force a structured check across multiple pages instantly, finding the exact breakdown point.
The honest tradeoffs
Using general searches for specific needs
Asking your agent simply, 'Show me running shoes.' This will return thousands of results and won't help you test if the store carries a size 10 in blue.
You need to combine tools. First, use search_products for the general term, then immediately apply search_filtered by specifying 'size: 10' and 'color: blue'. This combination gives you precise results.
Forgetting to check deeper categories
Only testing the main product page search box. You assume that if a department exists, it works perfectly.
You must use browse_category for major departments and then drill down using autocomplete within sub-categories to ensure full coverage of your product directory.
When It Fits, When It Doesn't
Use this MCP if your job requires verifying the logic of product discovery, not just running a simple search. You need it when you must confirm that filtering, sorting, and recommendation engines work together correctly. Don't use it if all you want is to know 'what products do you sell?'—just run search_products. However, if your goal is deep technical auditing (e.g., testing pagination or specific brand arrays), this MCP provides the necessary granular control via tools like search_pagination and browse_brand. The key difference is that general search tells you what exists; Constructor tells you how the system finds it.
Questions you might have
How do I test specific color and size combinations using search_filtered? +
You tell your agent you want to filter results by attributes. The tool uses search_filtered to restrict the product set, allowing you to check if only red, size 10 items appear, regardless of other criteria.
Is Constructor MCP better than just using search_products? +
Yes. While search_products finds general products, this MCP gives you the specialized tools to audit how those products are found—for example, checking if they are part of a specific brand line using browse_brand.
What is the difference between browse_category and search_products? +
A category browsing tool like browse_category maps out entire departments (e.g., 'Home Goods'). search_products performs a query against all records, regardless of their primary department.
Can I use the get_recommendations tool for marketing? +
Absolutely. You can simulate user paths and run get_recommendations to see which products should be promoted or featured in a specific marketing pod, validating your merchandising strategy.
How do I get started with `search_products` to test my e-commerce workflow? +
You first need to connect your Constructor.io API key in the MCP setup interface. This gives your agent the credentials it needs to run any search command against your live catalog data.
When should I use `search_pagination` instead of just running a general product search? +
Use this tool when you anticipate needing results beyond the initial page load. It automatically handles validation checks, so your agent can reliably fetch and process deep catalog data without hitting rate limits.
Can `browse_category` provide all available attributes for filtering? +
Yes, it generates a detailed JSON payload mapping the full taxonomy structure. This lets you map out every possible attribute and sub-classification within that category, which is crucial for advanced filtering logic.
What happens if I use `autocomplete` with an ambiguous search term? +
The tool extracts properties driving active account logic. It helps your agent narrow down the user’s intent by suggesting precise categories or brand names, making subsequent searches much more accurate.
Can my agent check the ML ranking for a specific product search? +
Yes. Use the 'search_products' tool. The agent will retrieve results ranked by Constructor's ML engine, allowing you to audit how products are surfaced based on specific keywords and intent signals.
How do I retrieve personalized recommendations via the agent? +
Provide the 'pod_id' to your agent and use the 'get_recommendations' tool. The agent will query the collaborative filtering models to return a list of products tailored to your specified recommendation logic.
Can I test attribute filtering like color or size through chat? +
Absolutely. The 'search_filtered' tool allows you to pass exact attribute mappings (e.g., 'color:blue,size:L'). Your agent will verify how the API restricts results to those specific structural bounds.
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