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Operant AI has launched MCP Gateway, an enterprise-grade runtime defense solution for AI applications utilizing the Model Context Protocol (MCP). * MCP Gateway functions as a secure intermediary between MCP-connected AI applications and the external resources they access. * Its features include real-time threat detection, contextual access control based on AI request context, and comprehensive audit and compliance logging. * Designed for scalable deployment, it offers developer-friendly integration via SDKs and APIs. * Operant AI aims to provide a critical security layer, enabling enterprises to safely leverage AI assistants that integrate deeply into business workflows through MCP.

Operant AI has launched MCP Gateway, a new security solution designed to provide enterprise-grade runtime defense for AI applications that utilize the Model Context Protocol. * MCP Gateway acts as a security layer for AI assistants, ensuring secure and compliant interactions with internal enterprise systems and data. * It offers features like real-time threat detection, data exfiltration prevention, and compliance enforcement for sensitive AI operations. * The solution integrates with existing enterprise security infrastructure and aims to address the unique security challenges of AI assistant integrations. * Operant AI highlights its role in enabling safe and scalable deployment of AI assistants that leverage the Model Context Protocol for tool use and data access.

Operant AI announced the expansion of its Gatekeeper platform with the introduction of the MCP Gateway. This new component focuses on providing runtime security for AI systems and large language models. * The MCP Gateway is designed to mediate and secure AI interactions with external tools and APIs. * It enforces policy controls, monitors data flows, and helps prevent unauthorized access or misuse by AI agents. * The platform aims to enhance enterprise-grade security, compliance, and governance for AI deployments. * It leverages the Model Context Protocol (MCP) to standardize and secure how AI models request and utilize external resources.

The article emphasizes the increasing need for AI assistants to integrate with external tools and data to enhance their utility. * It highlights the Model Context Protocol (MCP) by Anthropic as a crucial innovation in this area. * MCP allows AI assistants to access external resources, mimicking human use of tools or information retrieval. * The piece foresees a future where AI assistants extensively utilize plugins and tools to extend their core functionalities. * It underscores the concept of AI assistants serving as an 'external brain,' indicating the importance of sophisticated retrieval and context systems.

A new Model Context Protocol (MCP) server for WordPress has been developed, enabling AI assistants to automate blog post creation. The server allows AI assistants, specifically Claude, to directly interface with WordPress functionalities. It facilitates automated content generation, editing, and publishing, streamlining AI-driven workflows. The architecture positions WordPress as an MCP tool, accessible by MCP clients like Claude, demonstrating a practical application of MCP for robust AI workflow automation in content management.

New Relic has announced support for the Model Context Protocol (MCP) within its observability platform. * This integration allows developers and operations teams to gain deeper insights into AI applications, particularly those leveraging Large Language Models (LLMs). * The platform can now ingest data generated by MCP, providing a comprehensive view of AI assistant interactions and associated infrastructure. * New Relic's enhanced capabilities enable monitoring of token usage, model performance, and costs for AI applications, including those built with Anthropic's Claude. * The support for MCP aims to bridge the gap between AI development and production, offering better debugging and optimization tools for AI-driven systems.

Sentry has launched its own Model Context Protocol (MCP) server, designed to integrate with AI assistants like Claude on desktop. * The Sentry MCP server allows AI models to query an organization's Sentry data, enabling them to retrieve recent errors, performance issues, and detailed event information. * It leverages Sentry's public API to fetch real-time data, providing AI with up-to-date operational context for debugging and analysis. * Users can run the MCP server locally via `sentry-cli` or deploy it remotely, configuring their AI assistants to connect to it. * This integration aims to enhance AI's ability to act as a 'copilot' for developers, providing relevant Sentry insights directly within AI-driven workflows.

GitHub has announced the public preview availability of its remote Model Context Protocol (MCP) Server. This new server enables AI assistants and a wide range of AI tooling to directly access GitHub-hosted code, issues, pull requests, and other repository data through the MCP. The service is designed to facilitate deeper, more accurate context sharing for AI agents and workflows operating on GitHub resources. This development aims to significantly improve the accuracy, relevance, and overall performance of AI-powered developer tools within the broader developer ecosystem. The remote MCP Server is now accessible for all GitHub users to explore and integrate into their AI-driven processes, with ongoing enhancements and additional capabilities anticipated.

JetBrains WebStorm announced a livestream event focused on building a Model Context Protocol (MCP) server. The session, led by Ekaterina Petrova and Ekaterina Ryabtseva, demonstrated how to create an MCP server from scratch. Attendees learned to implement basic functionality within an existing Express.js application, including handling context retrieval and action execution requests. Key details: * The livestream was titled 'Building an MCP Server: Live Coding in WebStorm'. * It covered implementing MCP server capabilities within an Express.js application. * Participants learned how to expose data and actions to AI models. * The session showcased practical steps for setting up and debugging an MCP server in WebStorm.

The Model Context Protocol (MCP) is presented as a significant advancement for integrating AI more effectively into complex marketing workflows. MCP allows AI models to process and act upon rich, real-time contextual information, moving beyond the limitations of single prompt windows. * The protocol enhances AI's ability to access and utilize external data sources, tools, and internal enterprise systems. * It is critical for developing sophisticated AI agents capable of managing multi-step processes and dynamic data environments. * MCP supports the creation of AI assistants that can autonomously interact with various systems, improving aspects like content generation, personalization, and campaign management. * The article highlights MCP's role in enabling AI to deliver more precise and relevant outputs by providing a deeper understanding of the operational context.

New Relic has announced support for the Model Context Protocol (MCP), aiming to provide true end-to-end observability for AI applications. * The integration allows developers to trace user requests through large language models (LLMs) and their various integrations, offering unprecedented visibility into the AI application stack. * New Relic's platform will now ingest and process MCP data, enabling comprehensive monitoring of AI workflows from user input to LLM response and tool usage. * This support is crucial for debugging, performance optimization, and understanding the behavior of complex AI systems, especially those using tools and RAG. * It helps address the 'black box' challenge of LLMs by providing transparent insights into their operations and interactions within an application.

Varonis has announced its data security platform is the first to support the Model Context Protocol (MCP). * This integration enables AI assistants, such as Anthropic's Claude, to securely access and utilize sensitive enterprise data as context. * Varonis ensures that only authorized, non-sensitive information is provided to AI models via MCP, maintaining data privacy and compliance. * The new capability allows AI assistants to offer more accurate and relevant responses by incorporating secure enterprise context into their operations. * The development addresses the challenge of securely leveraging internal data for AI without exposing it or risking data leakage.