MCP News

Hypyr MCP Server now offers prompt analytics capabilities, providing developers with valuable insights into AI assistant interactions. * The analytics track metrics such as request counts, token usage (input/output), and cost per model for each prompt. * It allows for historical data analysis, identifying trends in prompt usage over time and the performance of different models. * Developers can leverage this data to optimize prompt engineering, manage costs, and improve the efficiency and accuracy of AI assistant integrations. * The feature aims to help developers understand how their AI assistants are utilized and make data-driven decisions for further refinement and development.

Cisco proposes a Dynamic Context Firewall (DCF) to enhance the security of AI interactions, specifically for AI assistants leveraging Anthropic's Model Context Protocol (MCP). * The DCF functions as an inline security layer, intercepting and analyzing the 'context object' exchanged between MCP clients (AI assistants) and external resources. * It validates and sanitizes external information, including tool definitions, API specifications, and knowledge bases, to control what AI models access. * The solution aims to mitigate critical security risks such as prompt injection, data exfiltration, and unauthorized access by AI agents. * By enforcing security policies on the dynamic context, the DCF protects both the AI model and integrated external systems.

The article details the implementation of Model Context Protocol (MCP) authorization using Spring AI and OAuth2. * It explains how MCP clients (AI assistants) can securely interact with MCP servers (tool providers) using OAuth2 for authentication and authorization. * The guide demonstrates configuring a Spring Boot application as an OAuth2 resource server and MCP server, exposing a tool that requires specific scopes. * It covers the setup of an OAuth2 authorization server and illustrates the flow of an MCP client obtaining an access token to call the secured MCP tool. * The content highlights the use of Spring AI's ToolFunction and ToolDescriptor annotations for defining and exposing AI tools securely.

The first malicious Model Context Protocol (MCP) server has been identified, posing a significant security risk to AI assistant integrations. * Security researchers discovered an MCP server actively distributing compromised context data to connected AI clients, including critical financial or personal information. * The malicious server exploited a previously unknown vulnerability in early MCP client implementations, allowing for data injection and potential privilege escalation. * The discovery prompted immediate alerts across the AI assistant ecosystem, with recommendations for client updates and enhanced server validation protocols. * Anthropic and other major AI platform providers have issued security advisories, urging developers to verify the authenticity and integrity of MCP servers before integration.

Fetch.ai has launched Agentverse, a new platform designed to accelerate the creation and deployment of AI agents. * A central feature of Agentverse is the Model Context Protocol (MCP), which facilitates secure and seamless interaction among AI models, data sources, and other agents. * MCP allows agents to dynamically discover and utilize external tools and services, significantly enhancing their operational capabilities. * The platform provides an SDK, developer tools, and a decentralized registry for publishing and discovering agents and services. * Fetch.ai aims to democratize the development of AI agents, enabling builders to easily integrate AI into a wide array of applications.

Klaviyo has introduced the concept of an 'MCP Server,' a universal API wrapper designed to help Large Language Models (LLMs) connect with external tools and data. * MCP Servers aim to bridge the gap between LLMs' reasoning capabilities and their ability to execute real-world actions by providing structured access to external systems. * The Model Context Protocol (MCP) is highlighted as crucial for enabling LLMs, particularly Claude, to understand and utilize tools effectively by translating tool descriptions into a machine-readable format. * The initiative addresses the challenge of making LLMs more actionable, moving beyond purely conversational interactions to integrate with operational workflows and retrieve specific information. * Klaviyo positions MCP Servers as a way to enhance AI assistants' utility by giving them programmatic access to a wide array of existing APIs and proprietary data sources.

Google Data Commons is utilizing an MCP Server to anchor AI models in verifiable facts, addressing the issue of AI hallucination. This integration provides AI systems with a reliable source of truth by leveraging Data Commons' extensive knowledge graph. * The initiative aims to combat AI's tendency to generate non-factual information. * It positions Google Data Commons as a critical infrastructure for providing structured, real-world data to AI. * The MCP Server facilitates AI assistants in grounding their responses in factual information, rather than speculative guesses. * This system enhances the trustworthiness and reliability of AI outputs by ensuring data provenance and accuracy.

Google has debuted its Data Commons Model Context Protocol (MCP) Server to provide trusted, grounded data for AI agents. * The MCP Server aims to address AI hallucinations by connecting agents to authoritative, real-world data sources. * It facilitates AI agents in performing complex, multi-step tasks that require up-to-date and verified information. * This initiative is part of a broader strategy to ensure AI assistants and agents operate with higher accuracy and reliability. * The server leverages Google's extensive Data Commons knowledge graph to offer a structured and verifiable context to AI models.

The article presents a comprehensive guide to implementing the Model Context Protocol (MCP) using Python. * It outlines MCP's role in facilitating AI assistant interaction with external tools and services. * The guide details the protocol's JSON-based structure for defining tools, requests, and responses. * Practical examples are given for developing MCP servers, which act as tool providers. * It also illustrates how AI assistants can effectively consume and integrate these MCP-defined tools.

Amazon has introduced the Amazon Redshift MCP Server, which implements Anthropic's Model Context Protocol (MCP) to enable AI agents to interact with Redshift data. This server acts as a tool provider, allowing AI assistants to generate, explain, and optimize SQL queries using a natural language interface. * The MCP Server facilitates a structured interaction between AI models and Redshift, transforming natural language requests into SQL and presenting results clearly. * It supports AI models in tasks such as data analysis, schema exploration, and query optimization, enhancing data accessibility for business users. * The architecture leverages Amazon Redshift Serverless and Amazon EKS to provide a scalable and secure environment for MCP interactions. * This integration offers a robust framework for building AI-driven data applications, improving productivity for data professionals.

Figma is launching "Make," a new initiative designed to deeply integrate AI into the design and development workflow, particularly focusing on AI-powered application coding. * The core functionality of "Make" is significantly supported by an update to an MCP (Model Context Protocol) server, enhancing the AI's ability to manage and utilize context for complex tasks. * The initiative aims to empower designers and developers to leverage AI for tasks like generating code snippets, automating design-to-code translations, and accelerating the creation of interactive prototypes directly within Figma. * This move signals a strategic shift towards embedding sophisticated AI tools, potentially relying on robust context management systems, into leading creative and development platforms. * "Make" seeks to streamline the entire app development lifecycle by enabling AI to assist in translating design concepts into functional application components more efficiently.