In release 232, we deliver a mix of new features, technical improvements, and editorial efficiency enhancements. Highlights include updates to MCP usage, Icecat Single Sign-On groundwork, the Assets Matching Tool preparation, improved product and coverage statistics, YouTube integration in multimedia, browser-based value reuse, tech stack exploration, and a range of other UI, security, and performance refinements. For additional details, please refer to the previous Icecat Release Notes.
In this sprint, our development team has delivered two important updates to the Model Context Protocol (MCP) server that directly support our long-term goals of transparency, research, and product evolution.
We have started collecting and providing daily reports to our internal stakeholders about the usage of the MCP server. This new reporting capability provides us with a clearer picture of user activity and is a crucial input for future improvements and decision-making. The insights gained will help us optimize performance, identify key usage patterns, and prioritize development areas based on actual data.
As part of our ongoing research phase, we are introducing a temporary limitation to MCP server access:
This measure allows us to conduct research in a more controlled environment and focus on validating the technical and business aspects of the MCP service without exposing the full product database.
It is important to underline that this is not a final decision. We remain flexible and open to revisiting this limitation once we complete our research and validation. We highly value the trust and partnership of our stakeholders. This temporary limitation should be seen as a research safeguard rather than a permanent change. We are fully open to discussions and negotiations with our esteemed partners to ensure alignment with mutual goals.
In this sprint, we successfully completed the development of an internal service for secured data retrieval. This milestone is a key step toward enabling Icecat Single Sign-On (SSO), which will greatly improve the user experience across our ecosystem.
The new internal service ensures that sensitive user data can be exchanged and validated in a secure, efficient, and controlled manner. It will serve as a foundation for future integration projects where authentication and data protection are essential.
One of the first use cases for this service will be the ability for Icecat users to log in to Hexagon using their existing Icecat account. This approach removes the need for multiple credentials and provides a smooth login experience while maintaining high security standards.
This development is not just a technical improvement – it is a strategic step towards unifying users management within the Icecat ecosystem.
In this sprint, we initiated the database preparation for our upcoming Assets Matching Tool. This functionality is designed to significantly enhance the efficiency and accuracy of our editorial processes.
The new tool will leverage predefined rules to streamline the handling of assets and their connection to product data sheets. Its core objectives are to:
As a foundational step, this sprint focused on preparing the parched feature values table, which will serve as the basis for optimized asset matching filters. This preparatory work ensures that the tool will perform with high speed and accuracy, while remaining scalable for future use cases.
In this sprint, we began collecting more detailed statistics about the usage of our APIs. This marks the first step toward ensuring that our systems are ready to support large-scale product imports in a reliable and efficient way.
The newly introduced monitoring focuses on:
This data collection phase will give us a comprehensive view of how our APIs are being used, providing the foundation for informed decision-making.
Once enough data has been collected, we will conduct a detailed analysis to identify:
These insights will guide us in making the system more robust and capable of handling large-scale product imports seamlessly.
In this sprint, we introduced an enhancement to our coverage analysis process to better handle non-standard product feed data.
Recently, we received a partner product feed containing emoji characters in product codes, brand names, and GTINs. This pattern suggests that the feed was generated by an AI-based tool, which often inserts special characters into text fields.
Since our Icecat database currently does not store any product codes or brand names with emoji, these entries presented an inconsistency in coverage analysis.
To ensure accurate reporting, we added a new condition to skip emoji-containing entries during coverage analysis. This improvement ensures that:
While this sprint focused on skipping unsupported data, we recognize that product feeds may evolve in the future. Therefore, we are preparing to upgrade our database format to support such special characters if they become relevant for real product data.
In this sprint, we enhanced the way we collect and analyze product usage statistics, bringing more clarity to how our services are being used.
The Icecat JSON service plays a key role in generating the Icecat Generated PDF. Until now, JSON requests linked to PDF generation were aggregated together with other JSON requests, which made it difficult to assess the specific use of this feature.
To address this, we introduced separate statistics tracking for cases when a non-authorized user requests an Icecat Generated PDF. With this improvement, we can:
This improvement provides us with more granular and actionable insights into how Icecat JSON is utilized. By isolating statistics for PDF generation requests, we are now better equipped to support data-driven decisions about future development of JSON services.
In this sprint, we delivered a small but impactful feature that boosts editorial efficiency and improves the user experience in the Icecat Brand Cloud product pages.
YouTube remains one of the most common sources of multimedia provided by our brand partners. These videos often already include valuable information, such as titles, that can enrich product pages if handled effectively.
To simplify and speed up the process for our editors, we implemented the following improvements:
These enhancements significantly reduce repetitive manual steps and ensure consistency across product pages. As a result, editors can focus more on content quality while the system takes care of routine tasks. For end users, the improvement means faster and more accurate multimedia integration, ensuring product data sheets are enriched with high-quality video content more efficiently.
In this sprint, we introduced a simple but highly effective feature designed to speed up the daily work of our editors, particularly those who describe multiple products within the same category.
The improvement applies to the specification block textual features – fields where editors can enter free-form information. Often, these fields are populated repeatedly with a limited set of predefined values across similar products. Until now, editors had to retype or copy-paste these values, which was both time-consuming and prone to inconsistencies.
With the new development мalues entered in text-type features are saved locally in the user’s browser. On subsequent product data sheets, editors can simply select from these previously entered values instead of typing them again.
This enhancement not only saves time but also reduces the risk of minor variations (e.g., typos, formatting differences) between similar products.
By streamlining repetitive input, editors gain increased efficiency when working with product families, consistency across product data sheets within the same category, and improved data quality, as predefined values help maintain accuracy and standardization
In this sprint, we took the first concrete steps in evaluating the potential of adding an extra programming language to our technology stack. The goal is to address obstacles that our current stack cannot efficiently resolve and to ensure Icecat systems remain robust, scalable, and adaptable to future needs.
Every technology stack has strengths and limitations. By introducing an additional language, we aim to:
As part of our research, we cloned the existing MCP service into an additional language using Claude Code and conducted several tests. These experiments allow us to:
This initiative reflects our commitment to future-proofing the Icecat platform. By carefully assessing options before making changes, we ensure that any decision to extend the stack will deliver clear value.
Alongside our major initiatives, this sprint also delivered a range of technical improvements and refinements across different areas of the Icecat ecosystem. These developments enhance reliability, performance, and maintainability of our platform.
We extended our autotest coverage to ensure higher quality and reliability for several data blocks in the Personal Catalog File (PCF) and Product CSV: Other Multimedia, PDF, 360, RichCSV. This improvement strengthens our testing foundation and reduces the risk of regressions in critical data handling functionalities.
To modernize our front-end codebase, we refactored several UI components by removing reliance on styled-components in favor of Tailwind CSS and simpler styling solutions. Updated components include: Spinner, MultiSelectFancy, MultiSelectCheckmarks, Scrollable, Button, DateSelect, TextInput. This shift simplifies styling, improves maintainability, and aligns the UI with a more consistent design system.
Resolved all ESLint warnings during the build process, resulting in cleaner, more maintainable code.
Prepared a plan for SEO and technical performance improvements for icecat.biz, ensuring both better visibility in search engines and smoother user experience.
Implemented DMARC for icecat.com, enhancing email security and reducing risks of phishing or spoofing.
Replaced the outdated “Open Catalog (URL)” link with the new “Open Catalog (JSON)” link, reflecting the modern data access method and improving clarity for users.
This release reinforces Icecat’s commitment to continuous improvement, informed decision-making, and efficient workflows. By combining data-driven insights, technical refinements, and enhancements to core services, we are building a platform that is more reliable, adaptable, and aligned with the needs of our users and partners.
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