Últimas Noticias

VS Code has announced the general availability of Model Context Protocol (MCP) support. This integration allows AI assistants and language models to access comprehensive contextual information directly from the VS Code workspace. * Enhanced context access includes open files, project structure, active editor selections, and terminal outputs, significantly improving AI response quality. * Developers can expect more accurate code generation, smarter completions, and highly relevant suggestions across their coding workflows. * The support facilitates deeper interoperability with various AI models and services designed to leverage the MCP standard. * This advancement provides developers with more powerful and context-aware AI assistance for a wide range of coding tasks.

Today, AWS announces the release of the Model Context Protocol (MCP) server for AWS Price List, now available in the AWS Labs GitHub repository. The MCP server provides AI agents with real-time access to AWS product data, availability information and pricing … MCP Relevance Analysis: - Relevance Score: 0.9/1.0 - Confidence: 0.95/1.0 - Reasoning: The article at the provided URL could not be retrieved, likely due to its future publication date (July 2025) or as a placeholder. However, based on the URL path '/model-context-protocol-server-price-list/', the implied topic of the article directly pertains to Model Context Protocol (MCP) servers and their pricing, which falls under direct MCP content and resource providers for the AI assistant ecosystem.

Microsoft is launching an integration of a Model Context Protocol (MCP) Server within Azure DevOps. * The MCP Server enables AI assistants to programmatically access and utilize context directly from Azure DevOps artifacts, including source code, work items, and pipelines. * This integration facilitates AI-driven tasks such as automated code review, intelligent issue tracking, and smart pipeline management. * It allows AI clients to connect to Azure DevOps for enhanced contextual understanding and more accurate interactions with developer workflows. * The development aims to improve developer productivity by providing AI assistants with richer, more relevant operational context.

The article outlines critical security considerations for building robust Model Context Protocol (MCP) servers, emphasizing the need for comprehensive protection against various threats. * It highlights authentication as a foundational layer, discussing token-based methods like OAuth2 and JWT for verifying client identity. * Authorization is crucial for controlling resource access, with the implementation of roles, permissions, and access control lists (ACLs) to manage what authenticated clients can do. * Data integrity and confidentiality are addressed through encryption (e.g., AES) and hashing for data-at-rest and TLS/HTTPS for data-in-transit, protecting sensitive context information. * Best practices include secure coding, regular security audits, managing secrets securely, and implementing rate limiting and input validation to mitigate common attack vectors.

Bitwarden has announced the development and upcoming release of its Model Context Protocol (MCP) Server. * The MCP Server is designed to securely integrate AI assistant platforms with Bitwarden vaults. * This allows AI models to access and manage sensitive credential information strictly under the Model Context Protocol. * It aims to enhance secure context delivery for AI, enabling new applications in personal and enterprise password management. * The server facilitates secure interaction for AI-driven tasks that require authenticated access to user data from a password manager.

AWS announced a new capability where Amazon S3 Tables are designed to operate as a Model Context Protocol (MCP) Server. This development enables AI assistants and MCP-compliant clients to directly access and retrieve structured context from data stored within S3 Tables. The integration aims to provide a scalable and efficient method for serving model context, leveraging the robust and widely adopted data storage capabilities of Amazon S3. This significantly enhances the AI assistant tooling ecosystem by facilitating seamless interaction between AI models and vast datasets hosted on AWS.

Bitwarden has launched its Model Context Protocol (MCP) server, designed to provide secure, programmatic access for AI agents and large language models (LLMs) to user credentials stored in Bitwarden vaults. * The MCP server acts as an intermediary, facilitating on-demand, just-in-time access to sensitive data for AI tools. * This solution addresses security and privacy concerns by ensuring AI agents only access necessary information when authorized. * It supports a range of use cases, from automating software development tasks to enhancing customer support. * Bitwarden highlights the importance of user consent and controlled data flow in AI interactions.

The article provides a detailed guide on building an application that leverages the Model Context Protocol (MCP). * It explains MCP as a specification enabling AI models to request external context and API calls from a client application during a conversation. * The tutorial demonstrates constructing an MCP client using AWS Lambda, Amazon API Gateway, and Mistral models hosted on Amazon Bedrock. * It walks through the MCP workflow, including the model's use of `tool_use` content blocks and the client's return of `tool_result` information. * A practical use case involving an order management system is presented as an example of external tool integration.

The rapid adoption of Model Context Protocol (MCP) servers by AI models is leading to a resurgence of common web vulnerabilities. * MCP servers are critical for AI systems to access real-time data and leverage external tools. * New MCP server implementations often quickly deploy web interfaces and APIs, overlooking fundamental security practices. * This rush results in flaws such as unauthenticated endpoints, broken access control, and directory traversal vulnerabilities. * These security weaknesses can enable data exfiltration, unauthorized system access, and novel forms of prompt injection affecting the AI models themselves.

The article provides a practical guide to building an MCP (Model Context Protocol) server within the Eclipse ECF (Eclipse Communication Framework) environment. It outlines the necessary steps for implementing an MCP server, specifically focusing on handling context requests from MCP clients. * The guide details the implementation of `IMCPService` and `IMCPContextService` interfaces. * It demonstrates how to register the MCP service with ECF's service registry using `AbstractMCPServiceFactory`. * The example includes code for creating a custom `IMCPContext` implementation to manage and provide context information. * The article illustrates the use of `IMCPContext#getHandles` and `IMCPContext#getContext` methods to serve context to clients, emphasizing the interaction between client requests and server responses.

The Eclipse Communication Framework (ECF) project is actively developing capabilities for building Model Context Protocol (MCP) servers to enhance AI assistant functionality. * ECF's existing OSGi-based remote services and messaging infrastructure are being leveraged to provide a robust foundation for MCP server creation. * A key focus is addressing the complexities of asynchronous operations and long-running tasks that arise when AI assistants interact with external tools via MCP. * The project aims to simplify the development of MCP servers, allowing developers to easily integrate various tools and resources for AI consumption. * Discussions are underway regarding the handling of streamed data and events, and the potential for bidirectional communication within the MCP framework.

A guide details using Supabase to build a Model Context Protocol (MCP) server. Supabase PostgreSQL is utilized for persistent storage of conversational context, tool definitions, and user data. Supabase Auth manages user authentication and authorization for MCP server access. Supabase Realtime facilitates instant updates and streaming of context or tool execution results. Supabase Edge Functions are deployed to handle MCP endpoint logic and integrate with external APIs, providing a scalable backend for AI assistant interactions.