Enterprises should experiment with MCP where it adds value, isolate dependencies and prepare for a multi-protocol future.

AWS가 AI 기반 코딩 에이전트를 활용하는 ‘서버리스 MCP 서버(Serverless MCP Server)‘를 공개했다. AWS에 따르면 해당 솔루션은 기업이 수동 개입을 최소화한 상태에서 관리형 애플리케이션을 더 빠르게 개발할 수 있도록 지원하기 위해 설계됐다. AWS는 서버리스 MCP 서버가 아마존 Q, 클라인(Cline), 커서(Cursor) 같은 AI 코딩 어시스턴트에게 서버리스 아키텍처에

MCP is becoming the plug-and-play standard for agentic AI apps to pull in data in real time from multiple sources. The Pulse MCP server directory currently lists more than 4,300 active MCP servers allowing LLMs to connect with

Long-time 9to5Mac readers will remember that the native Shortcuts app started as an indie project called Workflow, a clever, approachable tool that made automation fun and accessible to less technical users. Today, Shortcuts remains a powerful utility, particularly on the Mac. However, compared to how agentic AI tools have reshaped how we think about automation, it’s feeling a bit stagnant. That may be about to change. As reported by Mark Gurman in today’s Power On newsletter, Apple is preparing a significant overhaul of its Shortcuts app, integrating AI capabilities through its Apple Intelligence initiative: “A revamped version of its Shortcuts app, which today lets users create actions such as launching certain features within apps or playing a particular playlist. The new version will let consumers create those actions using Apple Intelligence models. (This had long been planned for 2025, but delays may push it to 2026.)” In other words, an AI-based Shortcuts app might allow more natural language interactions, further lowering the barrier for automation-curious users who might find the current interface a bit challenging to navigate. This move aligns with broader industry trends where AI is increasingly integrated into system-level functionalities. Microsoft’s Copilot+ PCs, for instance, feature an AI agent capable of performing actions and daisy-chaining system adjustments based on user requests through natural language commands. Imagine combining this capability with AI-generated automations triggered by simple voice or text prompts, such as: “When I start a Zoom, Teams, Meet, or FaceTime call, activate Do Not Disturb, dim the screen, and open Notes. During the meeting, capture participant names, job titles, and key discussion points. After the call, draft an email summarizing the meeting and outlining each person’s action items.” This might be trivial for you, but it sure isn’t for most users. And having this sort of stuff one voice command away could be really powerful. I’ll say it again: MCP And then there’s Anthropic’s MCP, an open standard designed to facilitate seamless integration between AI assistants and external data sources and tools. Against all odds, it has been adopted by major players, including Google and OpenAI (who also offer their own solutions), in a rare case of cross-industry collaboration. While it’s uncertain whether Apple will ever embrace MCP, its adoption would absolutely benefit users by enabling automation even further, beyond just system settings and limited local app control. Given Gurman’s report that this AI-powered Shortcuts feature, initially planned for 2025, might be delayed to 2026, it’s unlikely we’ll see it showcased at the June 9 WWDC keynote. Still, it’s a relief to know that Apple has been paying attention to this aspect of agentic AI. Whether it will deliver on its promise remains to be seen. Add 9to5Mac to your Google News feed.  FTC: We use income earning auto affiliate links. More.You’re reading 9to5Mac — experts who break news about Apple and its surrounding ecosystem, day after day. Be sure to check out our homepage for all the latest news, and follow 9to5Mac on Twitter, Facebook, and LinkedIn to stay in the loop. Don’t know where to start? Check out our exclusive stories, reviews, how-tos, and subscribe to our YouTube channel

The Desktop Commander MCP Server is a powerful tool that brings all your development operations into one chat interface. Built on top of the MCP Filesystem Server, it allows you to search, edit, and manage files, run terminal commands, and control processes directly from your desktop using the Model Context Protocol (MCP). Following are the core capabilities of the Desktop Commander MCP server: Terminal & Process Control Execute terminal commands with live output streaming Set timeouts and run commands in the background Manage sessions for long-running tasks List and kill running processes with detailed info Configuration Management Get or set server settings like: defaultShell (e.g., bash, zsh) blockedCommands (e.g., rm, shutdown) allowedDirectories for file access telemetryEnabled Apply changes without restarting the server Filesystem Operations Read and write files with line-based limits Append or overwrite file content Create and list directories Move or rename files and folders Get file and directory metadata Search files by name (case-insensitive) Code & Text Editing Perform precise text replacements (e.g., change config values) Rewrite entire files for major updates Search and replace patterns across multiple files Use vscode-ripgrep for fast recursive text/code search Audit Logging All actions are logged with timestamps and arguments Logs auto-rotate at 10MB to avoid clutter In this tutorial, we will be connecting Claude desktop with the MCP server and perform some tasks. Step 1: Setting up dependencies Node JS We need npx to run the Desktop Commander server, which comes with Node.js. Download the latest version of Node.js from nodejs.org Run the installer. Leave all settings as default and complete the installation Claude Desktop Download Claude using https://claude.ai/download. Step 2: Configuring the MCP Server Next, configure Claude to connect to your MCP server. Open the claude_desktop_config.json file located in the Claude installation directory using any text editor. If the file doesn’t exist, you can create it manually. Once opened, enter the following code: { "mcpServers": { "desktop-commander": { "command": "npx", "args": [ "-y", "@wonderwhy-er/desktop-commander" ] } } } Step 3: Running the server Once the MCP configuration is complete, your server should appear in Claude. The Desktop Commander server is a powerful interface, offering 18 tools for tasks like file management, terminal execution, process control, and more. Feel free to follow us on Twitter and don’t forget to join our 95k+ ML SubReddit and Subscribe to our Newsletter. Arham IslamI am a Civil Engineering Graduate (2022) from Jamia Millia Islamia, New Delhi, and I have a keen interest in Data Science, especially Neural Networks and their application in various areas.Arham Islamhttps://www.marktechpost.com/author/arhamislam/Step-by-Step Guide to Creating Synthetic Data Using the Synthetic Data Vault (SDV)Arham Islamhttps://www.marktechpost.com/author/arhamislam/Step-by-Step Guide to Create an AI agent with Google ADKArham Islamhttps://www.marktechpost.com/author/arhamislam/Implementing an LLM Agent with Tool Access Using MCP-UseArham Islamhttps://www.marktechpost.com/author/arhamislam/Implementing an AgentQL Model Context Protocol (MCP) Server

Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More Token Monster, a new AI chatbot platform, has launched its alpha preview, aiming to change how users interact with large language models (LLMs). Developed by Matt Shumer, co-founder and CEO of OthersideAI and its hit AI writing assistant Hyperwrite AI, Token Monster’s key selling point is its ability to route user prompts to the best available LLMs for the task at hand, delivering enhanced outputs by leveraging the strengths of multiple models. There are seven major LLMs presently available through Token Monster. Once a user types something into the prompt entry box, Token Monster uses pre-prompts developed through iteration by Shumer himself to automatically analyze the user’s input, decide which combination of multiple available models and linked tools are best suited to answer it, and then provide a combined response leveraging the strengths of said models. The available LLMs include: Anthropic Claude 3.5 Sonnet Anthropic Claude 3.5 Opus OpenAI GPT-4.1 OpenAI GPT-4o Perplexity AI PPLX (for research) OpenAI o3 (for reasoning) Google Gemini 2.5 Pro Unlike other chatbot platforms, Token Monster automatically identifies which LLM is best for specific tasks — as well as which LLM-connected tools would be helpful such as web search or coding environments — and orchestrates a multi-model workflow. “We’re just building the connectors to everything and then a system that decides what to use when,” said Shumer. For instance, it might use Claude for creativity, o3 for reasoning, and PPLX for research, among others. This approach eliminates the need for users to manually choose the right model for each prompt, simplifying the process for anyone who wants high-quality, tailored results. Feature highlights The alpha preview, which is currently free to sign up for at tokenmonster.ai, allows users to upload a range of file types, including Excel, PowerPoint, and Docs. It also includes features such as webpage extraction, persistent conversation sessions, and a “FAST mode” that auto-routes to the best model without user input. At the heart of Token Monster is OpenRouter, a third-party service that acts as a gateway to multiple LLMs, and into which Shumer has invested a small sum, by his admission. This architecture lets Token Monster tap into a range of models from different providers without having to build separate integrations for each one. Pricing and availability As of right now, Token Monster does not charge a flat monthly fee. Instead, users only pay for the tokens they consume through OpenRouter, making it flexible for varying levels of usage. According to Shumer, this model was inspired by Cline, a tool that enables high-spending users to access unlimited AI power, allowing them to achieve better outputs by simply using more compute resources. Multi-step workflows produce richer LLM responses Token Monster’s AI workflows extend beyond simple prompt routing. In one example, the chatbot might start with a research phase using web search APIs, pass that data to o3 for identifying information gaps, then create an outline with Gemini 2.5 Pro, draft text with Claude Opus, and refine it with Claude 3.5 Sonnet. This multi-step orchestration is designed to provide richer, more complete answers than a single LLM might be able to generate alone. The platform also includes the ability to save sessions, with data securely stored using the open source online database service Supabase. This ensures that users can return to ongoing projects without losing their work, while still giving them control over what data is saved and what is ephemeral. A non-traditional CEO In a notable experiment, Token Monster’s leadership has been handed over to Anthropic’s Claude model. Shumer announced that he is committed to following every decision made by “CEO Claude,” calling it a test to see whether an AI can manage a business effectively. “Either we’ve revolutionized management forever or made a huge mistake,” he wrote on X. Emerging from the Reflection 70-B controversy Token Monster’s launch comes less than a year after Shumer faced controversy over his launch and ultimate retraction of Reflection 70B, a fine-tuned version of Meta’s Llama 3.1 that was initially touted as the most highly performant open source model in the world, but which quickly became subject to criticism and accusations of fraud after third-party researchers were unable to reproduce its stated performance on third-party benchmark tests. Shumer apologized and said the issues were born out of mistakes made due to speed. The episode underscored the challenges and risks of rapid AI development and the importance of transparency in model releases. MCP integrations coming next Shumer said his team on Token Monster is also exploring new capabilities, such as integrating with Model Context Protocol (MCP) servers that allow websites and companies to have LLMs make use of their knowledge, tools, and products to achieve higher-order tasks than just text or image generation. This would enable Token Monster to connect with a user’s internal data and services, opening possibilities for it to handle tasks like managing customer support tickets or interfacing with other business systems. Shumer emphasized that Token Monster is still very much in its early stages. While it already supports a suite of powerful features, the platform remains an alpha product and is expected to see rapid iterations and updates as more users provide feedback. “We’re going to keep iterating and adding things,” he said. A promising experiment For users who want to take advantage of the combined power of multiple LLMs without the hassle of model switching, Token Monster could be an appealing choice. It’s designed to work for people who don’t want to spend hours tweaking prompts or testing different models themselves, instead letting the system’s automated routing and multi-step workflows handle the complexity. As Token Monster’s capabilities grow, it will be interesting to see how users and businesses adopt it — and how its experiment with AI-led management pans out. For now, it’s a promising addition to the rapidly expanding landscape of AI chatbots and digital assistants. Daily insights on business use cases with VB Daily If you want to impress your boss, VB Daily has you covered. We give you the inside scoop on what companies are doing with generative AI, from regulatory shifts to practical deployments, so you can share insights for maximum ROI. Read our Privacy Policy Thanks for subscribing. 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Reading Time: 5 minutes Last week, Google unveiled many interesting updates around Gemini at Google I/O 2025. The key highlight was the launch of Gemini 2.5 Pro, their most intelligent model yet. Although there were many updates, we’ve focused on those that will be beneficial for marketers like you. So, relax and read on to discover all the insights. Future of Google Search Shopping Google is reimagining how shoppers shop online—from product discovery to trial to checkout. If you sell online and list your products on Google, this is a huge update. The traditional method of shopping involved scanning hundreds of websites, reading reviews, and then buying a product. But with Google AI Mode or the Gemini app, shoppers can ask complicated questions, and Google will do the heavy lifting. Source – Google  For example, rather than searching for “best rugs for sofa”, shoppers can use Google AI mode and ask something like this, “I have a light gray sofa, I need a rug to brighten my room”. They can also add insights like “I have four kids at my house and they like to bring friends over”. But that’s not it, there’s more. Try it on Now, shoppers can try on clothes virtually to get a feel for how a style might look on them. Source – Google  They just need to upload your full-size image. While you might have seen something similar on other platforms, Google takes it to the next level with – Advanced 3D Shape Understanding Segmentation Diffusion There’s also a completely new way for checkout. Agentic Checkout Shoppers are always on the hunt for the best deals. Google’s new Agentic Checkout feature simplifies online shopping. It allows shoppers to specify their desired price, color, and size for a product, and Google will alert them when the item meets or drops below that price. When the price is dropped, shoppers can instantly check out with a click of a button, and all the details of your product (including size and colour) will already be added. Source – Google  Google examines numerous websites, sending alerts when its findings align with your specified preferences. If a price reduction occurs, a single click enables immediate checkout, as all your product specifics, such as size and color, are already saved. What does it mean for marketers? If you sell products online and list them on Google, you have a completely new channel through which your customers can discover, try, and purchase your products. However, optimize your products according to Google’s guidelines to ensure that they appear for relevant queries. Apparel companies must take advantage of the Try it on feature to provide their customers with a rich, personalized experience. Overall, E-commerce marketers can drive a lot of awareness, engagement, and conversions using the above updates. Gemini 2.5 Pro, AI Overviews and the New “AI Mode” Gemini 2.5 Pro is Google’s most intelligent, secure, and efficient LLM yet. Marketers can continue to use it for complicated tasks and deep research. It powers Google AI Overviews and the latest AI mode inside the Gemini App. Google AI Overviews is used by over 1.5 billion users every month across 200 countries. Rather than just providing links, AI Overviews provide direct, conversational answers and summaries. This change means that users get more information without clicking through, changing how visibility and traffic work. Seeing the rapid usage of AI Overviews, Google has launched something called “AI Mode.” They’ve reimagined Search from the ground up. Source – Google   Unlike a normal Google search, users can ask longer and more complex questions. The queries can be 2-3 times longer, and they can even ask follow-up questions. And the best part: It’ll take into account their personal information for even more personalized answers. AI Mode has already been launched in the US. What does it mean for marketers? To summarise, marketers now need to shift gears from just getting people to their website/app to making sure their content shows up in these AI-generated snippet/answers. Google has updated its generative AI capabilities, including image, audio, and video generation. Veo 3 now generates audio alongside video. Below is an example. What does it mean for marketers? Google is really pushing boundaries when it comes to what you can generate using AI. Marketers should definitely explore the various generative AI capabilities for improved campaign creation. However, if you’re looking to craft customer campaigns that resonate with your target customers and get higher engagement, you need to check out our own Generative AI platform, Merlin AI. There’s nothing better than an AI that constantly analyzes your previous campaigns to give you personalized output. Glance leveraged it for 50% faster campaign go-live times. Read the complete story here. Gemini Now Supports MCP The Gemini SDK now supports the Model Context Protocol (MCP), which was introduced by Anthropic. In addition, Google is bringing agentic capabilities to Search, Chrome, and the Gemini App. They call this “Agent Mode.” MCP is an emerging technical standard that allows AI models (like those used by Google) to connect more easily and securely with external, real-world data sources and tools. What does it mean for marketers? If you’re serious about building AI agents and you’ve been using Gemini, this is big news. The support for MCP opens up a new world of possibilities. But if you’re just getting started, you can read our latest on AI Agents for Customer Engagement. AI Agents can assist marketers with everything from campaign creation to analytics to decision-making. To help drive awareness, we’re also hosting a live masterclass titled “Agentic AI and Customer Engagement in 2025 and Beyond”. In this live panel discussion, we’ll cut through the noise and explore the exciting, practical ways to leverage AI in customer engagement. You can save your seat here. Android XR The most interesting launch was Google’s push into the XR space, the Gemini-powered Android XR platform. Designed for smart glasses and VR headsets, Android XR leverages Gemini for real-time translation, live messaging, contextual navigation, and more. Source – Google  What does it mean for marketers? Android XR is an emerging channel for marketers to consider. Its potential for rapid adoption suggests it could become a significant additional marketing avenue in the near future. Keep an eye out for future updates. Wrapping Up For marketers, the above updates mean that embracing AI-powered tools and strategies is no longer optional but essential for success. By leveraging these innovations, marketers can drive better results, improve customer engagement, and stay ahead of the curve in the ever-evolving world. The post Google I/O 2025 – What Marketers Need to Know appeared first on MoEngage.

Subscribe to Nintendo Life on YouTube814k After four years of service, Square Enix and FuturLab have released the last content update for PowerWash Simulator, titled 'The Muckingham Files 6'. Fittingly described as the "final stop" on the PowerWash journey, this free update leaves us with a mucky tube train in need of a scrubbing and the Sculpture Park — a new location where you'll have to blast away the dirt on a few pieces of artwork. Both of these fresh locales hide a handful of Easter eggs for PowerWash Simulator 2 (which still isn't confirmed for Switch 2, but we remain hopeful), so be sure to pay attention to the details. "We hope you enjoy this final PowerWash Simulator update as much as we have enjoyed creating it," a statement from FuturLab reads on the game's Steam page. "The team have poured their hearts and souls into creating a world for you all to escape to and clean, somewhere for you to relax and solo clean, or a place to gather with friends and catch up over a satisfying wash". The content update also ushered in a small patch, which targets a handful of bugs across the base game and DLC packs. You can find the full patch notes below: Improvements made to scenarios where a placeholder string would be present instead of the function key. Fixed a LOD issue in the Kennel in Wallace & Gromit’s House. Fixed a crash involving pushing the football in to the kennel in the Wallace and Gromit’s House job. Fixed the toy train sounds being affected by music settings in the Dining Room & Kitchen job. Fixed a scenario in Wallace & Gromit’s Dining Room & Kitchen where players could get stuck behind a step stool’s default position. Fixed an issue involving save file sizes on PS5. Fixed an issue where the lower part of Wallace & Gromit’s House was invisible when standing in a specific location. Fixed an issue on Switch involving water stream changing colour in Aim Mode in certain circumstances. Fixed an issue in Shrek jobs where surface targeted was not aligned in 3rd person view. Improvements made to players being able to get stuck inside the information board in the Duloc job. Fixed a scenario on Xbox where the title could crash when pressing the Back button during the credits. Fixed an issue involving equipment placement spots being visible during timelapses. Fixed an issue on Sony platforms where certain SFX were still audible after reducing Master Volume to zero. Fixed lighting inconsistencies in the kennel in the Wallace & Gromit DLC. Fixed flicking assets inside the kennel in the Wallace & Gromit DLC. Various other localisation improvements and minor bug fixes. So, there we have it, PowerWash Simulator has scrubbed up for the last time. Onto the sequel, eh? Though FutureLab's denying the rumours Feathers McPhwar What's your top PowerWash Simulator memory? Let us know in the comments. [source store.steampowered.com] Related Games See Also Share:0 1 Jim came to Nintendo Life in 2022 and, despite his insistence that The Minish Cap is the best Zelda game and his unwavering love for the Star Wars prequels (yes, really), he has continued to write news and features on the site ever since. Hold on there, you need to login to post a comment... Related Articles Nintendo Updates Switch 2 Backwards Compatibility List Doom Eternal, NBA 2K25, and more to get an update Nintendo Switch System Update 20.1.0 Is Now Live, Here Are The Full Patch Notes Stability is back, baby Animal Crossing: New Horizons Update 2.0.7 Patch Notes - Switch & Switch 2 Multiplayer Compatibility "Improved" Return to island life next week Nintendo Expands Switch Online's Game Boy Library With Four More Titles Available today Fantasy Life i Is Proving So Popular That Level-5 Has Already Announced Free DLC "We would like to express our deepest gratitude to everyone"

Real-world data is often costly, messy, and limited by privacy rules. Synthetic data offers a solution—and it’s already widely used: LLMs train on AI-generated text Fraud systems simulate edge cases Vision models pretrain on fake images SDV (Synthetic Data Vault) is an open-source Python library that generates realistic tabular data using machine learning. It learns patterns from real data and creates high-quality synthetic data for safe sharing, testing, and model training. In this tutorial, we’ll use SDV to generate synthetic data step by step. pip install sdv We will first install the sdv library: from sdv.io.local import CSVHandler connector = CSVHandler() FOLDER_NAME = '.' # If the data is in the same directory data = connector.read(folder_name=FOLDER_NAME) salesDf = data['data'] Next, we import the necessary module and connect to our local folder containing the dataset files. This reads the CSV files from the specified folder and stores them as pandas DataFrames. In this case, we access the main dataset using data[‘data’]. from sdv.metadata import Metadata metadata = Metadata.load_from_json('metadata.json') We now import the metadata for our dataset. This metadata is stored in a JSON file and tells SDV how to interpret your data. It includes: The table name The primary key The data type of each column (e.g., categorical, numerical, datetime, etc.) Optional column formats like datetime patterns or ID patterns Table relationships (for multi-table setups) Here is a sample metadata.json format: { "METADATA_SPEC_VERSION": "V1", "tables": { "your_table_name": { "primary_key": "your_primary_key_column", "columns": { "your_primary_key_column": { "sdtype": "id", "regex_format": "T[0-9]{6}" }, "date_column": { "sdtype": "datetime", "datetime_format": "%d-%m-%Y" }, "category_column": { "sdtype": "categorical" }, "numeric_column": { "sdtype": "numerical" } }, "column_relationships": [] } } } from sdv.metadata import Metadata metadata = Metadata.detect_from_dataframes(data) Alternatively, we can use the SDV library to automatically infer the metadata. However, the results may not always be accurate or complete, so you might need to review and update it if there are any discrepancies. from sdv.single_table import GaussianCopulaSynthesizer synthesizer = GaussianCopulaSynthesizer(metadata) synthesizer.fit(data=salesDf) synthetic_data = synthesizer.sample(num_rows=10000) With the metadata and original dataset ready, we can now use SDV to train a model and generate synthetic data. The model learns the structure and patterns in your real dataset and uses that knowledge to create synthetic records. You can control how many rows to generate using the num_rows argument. from sdv.evaluation.single_table import evaluate_quality quality_report = evaluate_quality( salesDf, synthetic_data, metadata) The SDV library also provides tools to evaluate the quality of your synthetic data by comparing it to the original dataset. A great place to start is by generating a quality report You can also visualize how the synthetic data compares to the real data using SDV’s built-in plotting tools. For example, import get_column_plot from sdv.evaluation.single_table to create comparison plots for specific columns: from sdv.evaluation.single_table import get_column_plot fig = get_column_plot( real_data=salesDf, synthetic_data=synthetic_data, column_name='Sales', metadata=metadata ) fig.show() We can observe that the distribution of the ‘Sales’ column in the real and synthetic data is very similar. To explore further, we can use matplotlib to create more detailed comparisons—such as visualizing the average monthly sales trends across both datasets. import pandas as pd import matplotlib.pyplot as plt # Ensure 'Date' columns are datetime salesDf['Date'] = pd.to_datetime(salesDf['Date'], format='%d-%m-%Y') synthetic_data['Date'] = pd.to_datetime(synthetic_data['Date'], format='%d-%m-%Y') # Extract 'Month' as year-month string salesDf['Month'] = salesDf['Date'].dt.to_period('M').astype(str) synthetic_data['Month'] = synthetic_data['Date'].dt.to_period('M').astype(str) # Group by 'Month' and calculate average sales actual_avg_monthly = salesDf.groupby('Month')['Sales'].mean().rename('Actual Average Sales') synthetic_avg_monthly = synthetic_data.groupby('Month')['Sales'].mean().rename('Synthetic Average Sales') # Merge the two series into a DataFrame avg_monthly_comparison = pd.concat([actual_avg_monthly, synthetic_avg_monthly], axis=1).fillna(0) # Plot plt.figure(figsize=(10, 6)) plt.plot(avg_monthly_comparison.index, avg_monthly_comparison['Actual Average Sales'], label='Actual Average Sales', marker='o') plt.plot(avg_monthly_comparison.index, avg_monthly_comparison['Synthetic Average Sales'], label='Synthetic Average Sales', marker='o') plt.title('Average Monthly Sales Comparison: Actual vs Synthetic') plt.xlabel('Month') plt.ylabel('Average Sales') plt.xticks(rotation=45) plt.grid(True) plt.legend() plt.ylim(bottom=0) # y-axis starts at 0 plt.tight_layout() plt.show() This chart also shows that the average monthly sales in both datasets are very similar, with only minimal differences. In this tutorial, we demonstrated how to prepare your data and metadata for synthetic data generation using the SDV library. By training a model on your original dataset, SDV can create high-quality synthetic data that closely mirrors the real data’s patterns and distributions. We also explored how to evaluate and visualize the synthetic data, confirming that key metrics like sales distributions and monthly trends remain consistent. Synthetic data offers a powerful way to overcome privacy and availability challenges while enabling robust data analysis and machine learning workflows. Check out the Notebook on GitHub. All credit for this research goes to the researchers of this project. Also, feel free to follow us on Twitter and don’t forget to join our 95k+ ML SubReddit and Subscribe to our Newsletter. Arham IslamI am a Civil Engineering Graduate (2022) from Jamia Millia Islamia, New Delhi, and I have a keen interest in Data Science, especially Neural Networks and their application in various areas.Arham Islamhttps://www.marktechpost.com/author/arhamislam/Step-by-Step Guide to Create an AI agent with Google ADKArham Islamhttps://www.marktechpost.com/author/arhamislam/Implementing an LLM Agent with Tool Access Using MCP-UseArham Islamhttps://www.marktechpost.com/author/arhamislam/Implementing an AgentQL Model Context Protocol (MCP) ServerArham Islamhttps://www.marktechpost.com/author/arhamislam/Implementing An Airbnb and Excel MCP Server

Many websites lack accessible and cost-effective ways to integrate natural language interfaces, making it difficult for users to interact with site content through conversational AI. Existing solutions often depend on centralized, proprietary services or require significant technical expertise, limiting scalability and adaptability. This creates a barrier for developers who want to implement intelligent agents capable of answering questions or assisting users using the site’s data. As a result, there is a need for an open, standardized approach that allows websites to expose structured information and support natural language interactions without relying heavily on external infrastructure or high-cost models.  Building conversational interfaces for websites remains a complex challenge, often requiring custom solutions and deep technical expertise. NLWeb, developed by Microsoft researchers, aims to simplify this process by enabling sites to support natural language interactions easily. By natively integrating with the Machine Communication Protocol (MCP), NLWeb allows the same language interfaces to be used by both human users and AI agents. It builds on existing web standards like Schema.org and RSS—already used by millions of websites—to provide a semantic foundation that can be easily leveraged for natural language capabilities. NLWeb is not a single tool or product but a suite of open protocols and open-source reference implementations designed to lay the groundwork for an AI-enabled web. Like HTML once did for document sharing, NLWeb envisions a shared infrastructure for integrating conversational AI into web content. Its sample code is a practical starting point rather than a final solution, encouraging community innovation and diverse implementations. This open, collaborative model draws inspiration from the early days of the internet, where shared standards and grassroots efforts drove rapid progress. NLWeb aims to do the same for AI-driven web experiences by enabling human-friendly interfaces and agent-to-agent communication through common protocols.  NLWeb consists of two main parts: a simple protocol for natural language interaction with websites, and a JSON-based response format that uses Schema.org. It includes an implementation that works well for sites structured as item lists—like products or reviews—and offers UI widgets to enable conversational access to such content. NLWeb also acts as an MCP (Model Context Protocol) server, letting AI models ask questions via a standardized “ask” method. Responses combine existing site data with insights from large language models, enhancing user interaction. NLWeb is open, cross-platform, and compatible with various AI models and vector databases, offering flexible integration options.  NLWeb offers web publishers a simple way to add conversational AI to their sites with minimal coding and without needing to build chatbots from scratch. It uses existing site data, ensuring accurate, real-time responses while keeping costs low. Publishers can choose which AI models to use and maintain control over their data. The system improves user engagement by enabling natural interactions, personalizing content, and enhancing support. Its open-source nature allows customization, and it positions websites for a future where AI agents browse and interact with the web.  In conclusion, NLWeb represents a foundational step toward a more interactive and intelligent web, where users can engage with websites through natural language rather than rigid interfaces. By combining structured data formats like Schema.org with the power of AI models, NLWeb simplifies the creation of conversational experiences. It empowers publishers to enhance their sites with minimal effort, offering benefits like improved user engagement, faster support, and personalized content delivery. As the web evolves into an ecosystem where AI agents play a growing role, NLWeb ensures that websites are not only accessible to humans but also seamlessly integrable with the agent-driven digital future.  Check out the GitHub Page. All credit for this research goes to the researchers of this project. Also, feel free to follow us on Twitter and don’t forget to join our 95k+ ML SubReddit and Subscribe to our Newsletter. Sana HassanSana Hassan, a consulting intern at Marktechpost and dual-degree student at IIT Madras, is passionate about applying technology and AI to address real-world challenges. 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