最新资讯与更新
A new Python-based tool, named 'MCP Scanner,' has been developed to address critical security vulnerabilities in AI models and agents. * The scanner is specifically designed to detect prompt injection attacks, a major concern for AI system integrity. * It aims to identify other security flaws that can lead to the creation of insecure AI agents. * The tool is intended to help developers and security professionals enhance the robustness and safety of AI assistant integrations, particularly those utilizing protocols like MCP. * Its release provides a dedicated resource for testing and hardening AI systems against common adversarial techniques.
GitHub has outlined its comprehensive offline evaluation strategy for the Model Context Protocol (MCP) Server, which is central to delivering relevant context to generative AI tools like Copilot Chat. * The MCP Server's primary function is to intelligently retrieve and provide contextual information from a user's workspace to large language models. * Evaluation relies on creating high-quality datasets of good context examples, alongside metrics like precision and recall to measure retrieval accuracy. * Human evaluators play a critical role, assessing the usefulness, accuracy, and completeness of the context retrieved by the server for various queries. * This continuous offline evaluation process is vital for iterating and improving the MCP Server, ultimately enhancing the quality and relevance of AI assistant responses.
AWS has announced new serverless tools specifically designed to support the Model Context Protocol (MCP). * These tools enable developers to deploy and manage MCP servers using AWS Lambda. * The new offering streamlines the process of building scalable and efficient backend services for AI assistant context provisioning. * It incorporates support for ECMA Script Modules (ESM), enhancing the developer experience for JavaScript-based MCP implementations.
An introduction to an MCP SDK for Clojure details the process of creating Model Context Protocol (MCP) services. The SDK aims to simplify developing tools that AI assistants, such as Claude Desktop, can discover and integrate. It outlines defining service descriptors and implementing `describe-capabilities` requests to advertise a service's functionalities. The guide includes practical Clojure code examples for constructing, packaging, and executing a basic MCP service, illustrating how to declare specific tools an AI can leverage. This facilitates the expansion of AI assistant capabilities through external, custom-built services.
The Janusian Genesis Chronicle details the concurrent evolution of specialized AI tools and generalist AI assistants. * The Model Context Protocol (MCP) is presented as foundational for enabling sophisticated multi-tool interactions and consistent context exchange for AI agents. * MCP's role extends to facilitating advanced integration concepts like Dynamic Tool Graphing and Cognitive Fabric Connectors. * These protocol-driven advancements are anticipated to enhance AI assistant platforms, including future Claude Desktop iterations. * Developer AI tools, such as advanced VS Code AI extensions and Aider-like systems, are expected to significantly benefit from robust MCP implementations.
Proximity is an open-source security scanner launched to help organizations secure their Model Context Protocol (MCP) implementations. * The tool identifies potential vulnerabilities in MCP server configurations, including improper access controls, insecure data handling, and misconfigurations. * It addresses the security risks introduced by MCP, developed by Anthropic, when AI models retrieve real-time information from external resources. * Proximity aims to assist developers and security teams in mitigating issues like data leakage and unauthorized access in AI systems utilizing MCP. * The scanner focuses on securing the API-like connections and external tooling that facilitate AI assistant interactions with outside data.
YouTrack has announced the introduction of a remote Model Context Protocol (MCP) server. * The new MCP server is designed to enhance YouTrack's integration capabilities with AI assistants and external tools. * A suite of new applications will be released, specifically built to leverage the MCP for improved context sharing. * The remote server architecture facilitates secure and efficient data exchange, allowing AI models to interact seamlessly with YouTrack data. * This initiative enables AI assistants to access project-specific information, automate issue tracking, and streamline development workflows within YouTrack environments.
Youtrack introduces a remote Model Context Protocol (MCP) server, designed to facilitate deep integration with AI assistants. * The new server enables AI clients to securely connect to Youtrack instances, allowing access to and manipulation of project management data. * AI assistants can now perform actions such as creating issues, updating tasks, querying project status, and retrieving user information directly within Youtrack. * This integration aims to enhance developer and team productivity by empowering AI to act as an intelligent agent within established project workflows. * New applications are released in conjunction with the MCP server, leveraging this integration to offer advanced AI-driven features for issue management and project oversight.
The Model Context Protocol (MCP) is a standard framework enabling AI models, such as Anthropic's Claude, to request and utilize external tools and information from their environment. * MCP clients formulate structured requests (ToolUse, ToolResult, Data) for specific actions or information from MCP servers. * MCP servers execute these requests, which can involve API calls, database interactions, or complex computations, returning results to the AI client. * The protocol establishes a clear separation between AI intent and tool execution, enhancing safety, reliability, and the overall capabilities of AI assistants. * MCP facilitates critical use cases, including real-time data access, complex computations, and seamless integration with various external systems.
The article outlines the process for setting up a Model Context Protocol (MCP) server designed to integrate AI capabilities for natural language tuning with SQL Server environments. It details the configuration steps required to establish an MCP server. * The content explores how to leverage artificial intelligence for interpreting natural language queries and commands, enabling more intuitive interaction with SQL Server databases. * Key methods for using AI to optimize SQL queries and enhance database performance are covered through this integration. * It discusses the implementation of Model Context Protocol for robust context management, crucial for effective AI applications within a data management framework.
Chrome DevTools has introduced new Model Context Protocol (MCP) server features, enabling AI models to directly access browser context for advanced web development tasks. * The MCP server allows AI, like Anthropic's Claude, to read the DOM structure, CSS, and network requests within Chrome. * This integration transforms AI assistants into powerful debugging, testing, and web page understanding tools for developers. * Developers can enable the MCP server in Chrome DevTools via `chrome://flags/#enable-dev-tools-gen-ai-experiments`. * The feature aims to provide AI models with real-time, comprehensive access to a webpage's state, enhancing AI-assisted coding and analysis.
Cisco has launched an open-source Model Context Protocol (MCP) Scanner to enhance the security of AI agent interactions. * The MCP Scanner functions as a 'firewall' for AI agents, verifying their use of external resources like APIs, databases, and filesystems against pre-defined security policies. * It helps mitigate risks such as data exfiltration, injection attacks, and unauthorized access within the AI agent supply chain by enforcing 'Policy as Code'. * Developers can use the tool to specify allowed API calls, network destinations, and access to sensitive information for their AI agents. * This project is available on GitHub and contributes to securing the rapidly evolving ecosystem of AI agents and their tool-using capabilities.