News from the AI & ML world
DeeperML - #mcp
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@pub.towardsai.net
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nordicapis.com
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Anthropic's Model Context Protocol (MCP) is rapidly gaining traction as a pivotal technology for AI agents, poised to revolutionize how these agents interact with external tools and APIs. MCP provides a standardized method for Large Language Models (LLMs) to access and utilize real-world services and data. This addresses a critical limitation of LLMs, which, while adept at processing information, traditionally lack the ability to directly trigger actions or retrieve live data from external sources. The protocol acts as a universal adapter, streamlining the integration of AI models with diverse tools and workflows, eliminating the need for custom integrations for each tool.
The MCP operates through a client-host-server architecture, enabling AI agents to discover available tools, invoke them as needed, and receive structured responses in a consistent format. This structured approach, using a declarative metadata model, greatly simplifies the development of scalable, tool-using AI agents and promotes efficient communication between the AI agent and the external resources. By standardizing the interaction process, MCP fosters autonomous consumption and multi-modal integrations, allowing AI agents to perform complex tasks with greater ease and efficiency. Netlify has embraced the Model Context Protocol with the release of its Netlify MCP Server, empowering AI agents to directly deploy code from within the development environment. This integration significantly enhances the agent experience, allowing AI agents to complete the entire development cycle, from code generation to deployment, without requiring manual intervention. The Netlify MCP Server provides agents with direct access to the Netlify API and CLI, enabling them to create projects, manage infrastructure, and deploy applications using natural language prompts. This capability marks a significant step towards seamless AI-driven development workflows, transforming the way developers interact with their tools and infrastructure. Recommended read:
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@orases.com
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AI agents are rapidly transforming industries by automating tasks and enhancing decision-making, moving beyond simple automation to intelligent autonomy. These agents are being implemented across various sectors, promising significant improvements in efficiency and productivity. A strategic roadmap is essential for successful AI agent implementation, aligning technology with workflows and business objectives to ensure that these systems have a real impact on operations and decision-making. Without a clear structure, companies risk wasting investments on generic tools and isolated pilot projects.
The impact of AI agents is particularly evident in customer experience (CX), with companies increasingly integrating AI agents into their technology interactions. Cisco's recent Agentic AI Report highlights the transformative impact of these autonomous agents, which can retain memory, reason about tasks, and autonomously select actions to optimize outcomes with minimal human intervention. Cisco's data anticipates that enterprises expect 56% of their interactions with technology partners will be managed by AI agents within the next 12 months, increasing to 68% over three years. This accelerated adoption necessitates that vendors rapidly develop and deploy scalable, robust agentic AI solutions. Thomson Reuters is also leveraging this trend with agentic AI capabilities in its CoCounsel assistant, enabling autonomous, multi-step task execution in tax and accounting workflows. Early results show that processes like tax jurisdiction reviews have been drastically reduced from half a week to under an hour. The company plans to extend agentic AI to legal, risk, and compliance domains, connecting firm knowledge, codes, and internal documents into one workspace where AI handles complete workflows, rather than individual queries. This integration allows professionals to focus on higher-level tasks, ensuring that human expertise guides judgment and validates outputs. Recommended read:
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Priyansh Khodiyar@CustomGPT
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CustomGPT
, hackernoon.com
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The Model Context Protocol (MCP) is gaining momentum as a key framework for standardizing interactions between AI agents and various applications. Developed initially by Anthropic, MCP aims to provide a universal method for AI models to connect with external tools, data sources, and systems, similar to how USB-C streamlines connections for devices. Microsoft is actively embracing this protocol, introducing MCP servers for its Dynamics 365 platform. Furthermore, companies are integrating MCP into their APIs, indicating a widespread movement towards its adoption.
The core challenge MCP addresses is the current fragmented and inconsistent nature of AI integrations. Without a standardized protocol, developers often resort to custom code and brittle integrations, leading to systems that are difficult to maintain and scale. MCP standardizes how context is defined, passed, and validated, ensuring that AI agents receive the correct information in the right format, regardless of the data source. This standardization promises to alleviate the "It Works on My Machine… Sometimes" syndrome, where AI applications function inconsistently across different environments. MCP's adoption is expected to pave the way for more autonomous enterprises and smarter systems. Microsoft envisions a future where AI agents proactively identify problems, suggest solutions, and maintain context across conversations, thereby transforming workflows across diverse fields such as marketing and software engineering. The evolution of identity standards, particularly OAuth, is crucial to secure agent access across connected systems, ensuring a robust and reliable ecosystem for AI agent interactions. This collaborative effort to build standards will empower the next generation of AI agents to operate effectively and securely. Recommended read:
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@www.microsoft.com
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Microsoft is aggressively expanding its AI integration across its product ecosystem. Recent announcements highlight the company's efforts to embed AI into core applications like Windows Notepad and Dynamics 365, as well as leverage AI for advanced solutions like weather forecasting with its Aurora model. A key component of this strategy is the Model Context Protocol (MCP), which is being implemented in Windows 11 to facilitate secure and standardized interactions between AI agents, applications, and system tools. These initiatives demonstrate Microsoft's commitment to reshaping how users interact with technology, aiming to enhance productivity and automate complex processes across both enterprise and consumer environments.
Microsoft's AI push includes the integration of Copilot into the Windows Notepad application, enabling AI-driven text generation and refinement directly within the text editor. This update, while raising questions about its necessity for such a basic tool, reflects Microsoft's broader ambition to infuse AI capabilities into even its most established and simple software. Additionally, the introduction of Model Context Protocol (MCP) servers for Microsoft Dynamics 365 ERP and CRM business applications signals a major step towards creating "agent-ready" business applications. MCP will remove the need to manually connect systems together to build agents and accelerate the ability for customers and partners to build AI-powered agents. The goal is to allow AI agents to operate seamlessly across various business processes, industries, and segments, making businesses more efficient. Microsoft's Aurora AI model showcases the potential of AI to revolutionize specialized domains like weather forecasting. Aurora is designed to provide detailed and accurate 10-day forecasts in seconds, a task that traditionally takes hours using conventional models. This breakthrough not only promises faster and more precise weather predictions but also demonstrates the model's versatility, as it can be trained to forecast other environmental elements like air pollution and cyclones. Furthermore, the implementation of MCP in Windows 11 focuses on enabling AI agents to interact with applications and system tools, with security measures in place. This move aims to transform Windows 11 into an "agentic" platform, where AI agents can carry out tasks across apps, files, and services without needing manual inputs. Recommended read:
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@www.eweek.com
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Microsoft is embracing the Model Context Protocol (MCP) as a core component of Windows 11, aiming to transform the operating system into an "agentic" platform. This integration will enable AI agents to interact seamlessly with applications, files, and services, streamlining tasks for users without requiring manual inputs. Announced at the Build 2025 developer conference, this move will allow AI agents to carry out tasks across apps and services.
MCP functions as a lightweight, open-source protocol that allows AI agents, apps, and services to share information and access tools securely. It standardizes communication, making it easier for different applications and agents to interact, whether they are local tools or online services. Windows 11 will enforce multiple security layers, including proxy-mediated communication and tool-level authorization. Microsoft's commitment to AI agents also includes the NLWeb project, designed to transform websites into conversational interfaces. NLWeb enables users to interact directly with website content through natural language, without needing apps or plugins. Furthermore, the NLWeb project turns supported websites into MCP servers, allowing agents to discover and utilize the site’s content. GenAIScript has also been updated to enhance security of Model Context Protocol (MCP) tools, addressing vulnerabilities. Options for tools signature hashing and prompt injection detection via content scanners provide safeguards across tool definitions and outputs. Recommended read:
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Ken Yeung@Ken Yeung
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pub.towardsai.net
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HubSpot has launched a public beta of its Model Context Protocol (MCP) server, empowering developers to integrate AI applications with customer relationship management (CRM) data. This offering, provided as an NPM package, is specifically designed for developers, technical teams, and businesses seeking to create custom applications or integrations using large language models (LLMs). The MCP server allows AI applications like Claude and Cursor to directly access HubSpot data, facilitating tasks such as retrieving, creating, or modifying HubSpot objects, listing properties, and generating tasks and notes using natural language. This allows users to streamline development and gain insights, demonstrated by examples such as summarizing deals or updating customer information.
The Model Context Protocol standardizes the way AI models connect to applications through a consistent interface. HubSpot describes MCP as an abstraction layer over traditional APIs, enabling AI agents to access application functionality without needing to understand specific API protocols. This mirrors the function of API feeds from companies like Google, Facebook, and Salesforce, providing a standardized connection point in the AI era. By opening its MCP server, HubSpot aims to equip small and medium-sized businesses (SMBs) with the tools to leverage AI in a manner previously accessible only to larger enterprises. Microsoft CEO Satya Nadella has also endorsed the Model Context Protocol, signaling a shift towards open standards for AI interoperability. Nadella’s support, along with his endorsement of Google DeepMind's Agent2Agent (A2A) protocol, is expected to accelerate AI-based collaboration and the development of agentic AI applications. He announced upcoming support for A2A and MCP in Copilot Studio and Foundry, emphasizing that open protocols are key to enabling the agentic web. This move aligns with Nadella's history of championing open, interoperable AI architectures, promoting the idea that open standards, rather than proprietary silos, drive the adoption of new AI technologies. Recommended read:
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@www.dremio.com
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The Model Context Protocol (MCP) is emerging as a crucial standard for streamlining AI agent tool calling, addressing the growing challenges of data silos and integration complexities within organizations. As businesses increasingly implement AI across various departments, they encounter difficulties integrating data from disparate systems, hindering efficient AI deployment. Traditionally, organizations have relied on ad-hoc, model-specific integrations, which are time-consuming and difficult to maintain, secure, and scale. This approach often involves creating individual connectors for new integrations, becoming impractical as AI applications expand throughout the business.
The Model Context Protocol offers a paradigm shift by standardizing how AI agents access and utilize external tools such as APIs and databases. MCP aims to revolutionize how AI systems connect with data sources and other AI systems by acting as a unified gateway for accessing a range of web data and web APIs. This open standard aims to enable secure and interoperable workflows, simplifying the integration process and allowing businesses to focus on tool selection and application rather than custom integration code. MCP simplifies tool integration, enabling customers to focus on which tools to use and how to use them. Several organizations and platforms are embracing MCP to enhance AI capabilities. For example, Apify offers a marketplace of pre-built tools (called "Actors") designed to interact with websites and extract data, which can be seamlessly integrated with applications like Claude desktop through MCP. Docker has introduced the Docker MCP Catalog and Toolkit to simplify the discovery, installation, and security management of MCP servers. Furthermore, investments from Databricks and KPMG in LlamaIndex demonstrate the growing importance of handling unstructured data and enabling Retrieval-Augmented Generation (RAG) applications, positioning LlamaIndex at the center of an essential transformation in enterprise data intelligence. Recommended read:
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@blogs.microsoft.com
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Microsoft is aggressively promoting agentic AI as a key driver for business transformation, emphasizing its potential to unlock greater value for customers. Agentic AI, with its autonomous capabilities, combined with copilots and human ambition, is believed to offer real AI differentiation. Microsoft's vision involves embedding AI directly into business processes, enabling organizations to achieve more by leveraging the power of intelligent agents acting on their behalf. Judson Althoff, Executive Vice President and Chief Commercial Officer at Microsoft, highlighted the rapid growth of agentic AI and its crucial role in accelerating AI transformation for businesses. The recent introduction of Microsoft 365 Copilot further underscores this commitment to making AI accessible and beneficial to all.
Recent updates include the release of a comprehensive guide to failure modes in agentic AI systems by Microsoft's AI Red Team (AIRT). This guide aims to help practitioners design and maintain resilient agentic systems by addressing potential security and safety challenges. The guide categorizes failure modes across two dimensions: security and safety, each comprising both novel and existing types. Novel security failures include agent compromise, agent injection, and agent flow manipulation, while novel safety failures cover intra-agent Responsible AI concerns and biases in resource allocation. By providing a structured analysis of these failure modes, Microsoft seeks to foster the responsible development and deployment of agentic AI technologies. In addition to agentic AI, Microsoft is also urging the U.S. and its allies to double down on quantum computing investments to maintain technological leadership amid growing global competition. Microsoft President Brad Smith warned that the U.S. risks falling behind China in the quantum race unless it strengthens investment, workforce development, and supply chain security. Smith advocates for expanding federal research funding, boosting quantum talent development, and shoring up domestic quantum manufacturing. He emphasized that quantum computing is transitioning from theory to practice, with transformative potential for science, medicine, energy, and national security. Recommended read:
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@cloudnativenow.com
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Docker, Inc. has embraced the Model Context Protocol (MCP) to simplify the integration of AI agents into container applications. The company has introduced both an MCP Catalog and an MCP Toolkit, aiming to provide developers with tools to effectively manage and utilize MCP-based AI agents. This move is intended to allow developers to leverage existing tools and workflows when incorporating artificial intelligence capabilities into their applications, making the process more streamlined and efficient.
Docker's MCP Catalog, integrated into Docker Hub, offers a centralized location for developers to discover, run, and manage MCP servers from various providers. It currently features over 100 MCP servers from providers such as Grafana Labs, Kong, Inc., Neo4j, Pulumi, Heroku, and Elastic Search, accessible directly within Docker Desktop. Future updates to Docker Desktop will include features that enable application development teams to publish and manage their own MCP servers, with controls such as registry access management (RAM) and image access management (IAM), as well as secure secret storage. Nikhil Kaul, vice president of product marketing for Docker, Inc., emphasized the company's commitment to empowering application developers to build the next generation of AI applications without disrupting their existing tooling. The goal is to make it easier for developers to experiment and integrate AI capabilities into their workflows. Docker's earlier initiatives, such as the Docker Model Runner extension for running large language models (LLMs) locally, demonstrate a consistent approach to simplifying AI integration for developers. Recommended read:
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Adit Sheth@towardsai.net
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The Model Context Protocol (MCP) is rapidly gaining traction as a crucial standard for enabling AI agents to effectively interact with real-world tools and applications. Developed by Anthropic and supported by major players like OpenAI, Microsoft, and Dremio, MCP aims to standardize how agents interact with various systems and data sources, acting as a "USB-C for AI applications." This allows AI agents to work more independently, learn, adapt, and plan autonomously, expanding their capabilities beyond traditional AI tasks by facilitating communication with external tools and services.
MCP addresses the problem of context fragmentation in AI development. Currently, AI agents often need custom code for each tool they interact with, leading to complexity and scalability issues. MCP provides a single interface for agents to connect to, translating requests and routing them to the appropriate tool behind the scenes. This simplifies integration and creates a cleaner, more scalable setup. The protocol enables agents to discover available capabilities, understand how to use them, and invoke them dynamically in real-time, streamlining the process of accessing and utilizing external resources. Examples of MCP's versatility include enabling AI agents to exchange multimedia messages on WhatsApp, conduct deep web searches, generate music from prompts, and design user interfaces with Figma. For instance, Dremio utilizes MCP to allow agents to access and interact with structured data through SQL, while also translating natural language queries into executable SQL. Auth0 integrates with MCP to provide secure AI agents with identity and access controls. The LangChain MCP adapter further simplifies development by allowing developers to build MCP agents using Composio's managed MCP server, demonstrating the growing ecosystem and its potential to transform AI application development. Recommended read:
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@The Official Google Blog
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Google has been actively enhancing its AI capabilities across various platforms, including Gmail and its Gemini models. The tech giant recently released an AI-powered update to Gmail's search functionality, shifting from chronological results to a relevance-based ordering. This update analyzes emails based on factors like recency, frequently clicked emails, and frequent contacts to prioritize conversations users are most likely looking for. AWISEE.com, an influencer marketing agency, has analyzed how this update could enhance influencer outreach, making it easier to find important emails, streamline follow-ups, and enhance organization for large-scale outreach efforts. This improves communication and strengthens relationships for brands and agencies using influencer marketing.
Expanding its AI prowess, Google has also broadened access to Gemini 2.5 Pro, its latest flagship AI model. Alphabet CEO Sundar Pichai touts Gemini 2.5 Pro as Google's "most intelligent model + now our most in demand." The model boasts strong performance in scientific testing, achieving an 84% score on the GPQA Diamond benchmark, surpassing the average human expert score. Google is offering competitive pricing for Gemini 2.5 Pro, making it more accessible to developers. Google plans to unveil new Gemini models ahead of the Cloud Next event scheduled for April 9-11. There is also speculation around new experimental options, similar to the 2.0 Flash "thinking-with-apps" approach, which introduced models dedicated to specific use cases. Also in development are features like scheduled prompts and video generation. Gemini 2.5 Pro is now available without limits and for cheaper than Claude, GPT-4o Recommended read:
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Ryan Daws@AI News
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Anthropic has unveiled a novel method for examining the inner workings of large language models (LLMs) like Claude, offering unprecedented insight into how these AI systems process information and make decisions. Referred to as an "AI microscope," this approach, inspired by neuroscience techniques, reveals that Claude plans ahead when generating poetry, uses a universal internal blueprint to interpret ideas across languages, and occasionally works backward from desired outcomes instead of building from facts. The research underscores that these models are more sophisticated than previously thought, representing a significant advancement in AI interpretability.
Anthropic's research also indicates Claude operates with conceptual universality across different languages and that Claude actively plans ahead. In the context of rhyming poetry, the model anticipates future words to meet constraints like rhyme and meaning, demonstrating a level of foresight that goes beyond simple next-word prediction. However, the research also uncovered potentially concerning behaviors, as Claude can generate plausible-sounding but incorrect reasoning. In related news, Anthropic is reportedly preparing to launch an upgraded version of Claude 3.7 Sonnet, significantly expanding its context window from 200K tokens to 500K tokens. This substantial increase would enable users to process much larger datasets and codebases in a single session, potentially transforming workflows in enterprise applications and coding environments. The expanded context window could further empower vibe coding, enabling developers to work on larger projects without breaking context due to token limits. Recommended read:
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Maximilian Schreiner@THE DECODER
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OpenAI has announced it will adopt Anthropic's Model Context Protocol (MCP) across its product line. This surprising move involves integrating MCP support into the Agents SDK immediately, followed by the ChatGPT desktop app and Responses API. MCP is an open standard introduced last November by Anthropic, designed to enable developers to build secure, two-way connections between their data sources and AI-powered tools. This collaboration between rivals marks a significant shift in the AI landscape, as competitors typically develop proprietary systems.
MCP aims to standardize how AI assistants access, query, and interact with business tools and repositories in real-time, overcoming the limitation of AI being isolated from systems where work happens. It allows AI models like ChatGPT to connect directly to the systems where data lives, eliminating the need for custom integrations for each data source. Other companies, including Block, Apollo, Replit, Codeium, and Sourcegraph, have already added MCP support, and Anthropic's Chief Product Officer Mike Krieger welcomes OpenAI's adoption, highlighting MCP as a thriving open standard with growing integrations. Recommended read:
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Maximilian Schreiner@THE DECODER
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OpenAI has announced its support for Anthropic’s Model Context Protocol (MCP), an open-source standard. The move is designed to streamline the integration between AI assistants and various data systems. MCP is an open standard that facilitates connections between AI models and external repositories and business tools, eliminating the need for custom integrations.
The integration is already available in OpenAI's Agents SDK, with support coming soon to the ChatGPT desktop app and Responses API. The aim is to create a unified framework for AI applications to access and utilize external data sources effectively. This collaboration marks a pivotal step towards enhancing the relevance and accuracy of AI-generated responses by enabling real-time data retrieval and interaction. Anthropic’s Chief Product Officer Mike Krieger welcomed the development, noting MCP has become “a thriving open standard with thousands of integrations and growing.” Since Anthropic released MCP as open source, multiple companies have adopted the standard for their platforms. CEO Sam Altman confirmed on X that OpenAI will integrate MCP support into its Agents SDK immediately, with the ChatGPT desktop app and Responses API following soon. Recommended read:
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Maximilian Schreiner@THE DECODER
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OpenAI and Anthropic, competitors in the AI space, are joining forces to standardize AI-data integration through the Model Context Protocol (MCP). Introduced by Anthropic last November, MCP is an open standard designed to enable developers to build secure, two-way connections between data sources and AI-powered tools. The protocol allows AI systems like ChatGPT to access digital documents and other data, enhancing the quality and relevance of AI-generated responses. MCP functions as a "USB-C port for AI applications," offering a universal method for connecting AI models to diverse data sources and supporting secure, bidirectional interactions between AI applications (MCP clients) and data sources (MCP servers).
With OpenAI's support, MCP is gaining momentum as a vendor-neutral way to simplify the implementation of AI agents. Microsoft and Cloudflare have already announced support for MCP, with Microsoft adding it to Copilot Studio. This collaboration aims to improve AI interoperability by providing a standard way for AI agents to access and retrieve data, streamlining the process of building and maintaining agents. The goal is to enable AI agents to take actions based on real-time data, making them more practical for everyday business use, with companies like Databricks aiming to improve the accuracy of AI agents to above 95 percent. Recommended read:
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