News from the AI & ML world
DeeperML - #mcp
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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@Towards AI
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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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Adarsh Menon@Towards AI
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Composio
, Thomas Roccia :verified:
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Anthropic's Model Context Protocol (MCP), released in November 2024, is gaining significant traction in the AI community. This protocol is designed as a standardized method for connecting AI assistants with the systems where data resides, including content repositories, business tools, and development environments. MCP facilitates a consistent manner for applications to provide context to Large Language Models (LLMs), effectively isolating context provision from direct LLM interaction. Thomas Roccia, among others, recognized the value of MCP for AI agents immediately upon its release.
MCP acts as a universal set of rules, enabling seamless communication between clients and servers, regardless of their origin. This interoperability lays the groundwork for a diverse AI ecosystem. It defines how clients interact with servers and how servers manage tools and resources. The protocol aims to standardize the integration of context and tools into AI applications, analogous to the USB-C port for agentic systems, as described by Anthropic. Recommended read:
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