MCP News

Amazon CloudWatch Application Signals has introduced updates, including a new GitHub Action and enhancements to its MCP Server. * A new GitHub Action has been added to enable automated instrumentation of applications. * Improvements have been made to the MCP Server, focusing on enhancing application monitoring capabilities. * These updates aim to streamline the integration of Application Signals into existing CI/CD pipelines. * The enhancements are designed to boost the overall performance and reliability of the MCP Server.

The Model Context Protocol (MCP) emerges as a new paradigm, extending beyond traditional APIs to provide AI models with a deeper understanding of their execution environment. * MCP allows AI assistants to 'see' and interact with the full context of an application or operating system, rather than relying on abstract API calls. * It facilitates AI models understanding UI elements, code execution, and user workflows directly, enabling more intelligent and context-aware actions. * Key components include MCP Servers (tool/resource providers like VS Code, browsers) and MCP Clients (AI assistants like Claude Desktop). * MCP promises to unlock new capabilities for AI in development, data analysis, and workflow automation, making AI assistants more powerful and integrated into human-computer interaction.

Elastic Path announced the general availability of its Developer MCP Server. * The server offers a standardized interface for AI assistants and large language models to integrate with the company's commerce platform. * It allows developers to build custom tools for AI, enabling functions such as product lookup, order management, and personalized recommendations. * The initiative aims to enhance AI assistant extensibility by providing structured, real-time access to e-commerce data and functions. * This release supports the expanding ecosystem of AI-powered applications and agent frameworks.

Real Python has introduced a guide to the Model Context Protocol (MCP) Python client library. MCP enables large language models (LLMs) to access and interact with local development environments, providing crucial context. * The Python client facilitates communication between LLMs, such as Claude, and local MCP servers. * Users can configure their AI assistants with an `mcp` tool to retrieve file contents, execute commands, and understand the repository state. * The guide outlines the installation of the `mcp-client` library, setting up a local MCP server, and integrating the `mcp` tool with an AI assistant. * This setup allows AI models to perform context-aware operations, significantly enhancing their utility in developer workflows.

This quiz covers the implementation and usage of a Python Model Context Protocol (MCP) client. * It focuses on the `ModelContextClient` class, which allows AI models to interact with external tools and the real world. * The content explains defining tools using `ToolSpecification` and `ToolParameter` objects, along with executing tool calls via `client.call()`. * It details handling tool outputs and errors, emphasizing the role of `AgentContext` in client operations. * The quiz clarifies how prompts guide tool interactions within the MCP client framework.

Grasshopper Bank and Narmi have expanded their Model Context Protocol (MCP) capabilities to enhance security for AI tool integrations, specifically including ChatGPT, within their digital banking platforms. * The enhancement focuses on boosting security by strictly controlling AI model access to sensitive data and ensuring adherence to compliance and privacy regulations. * MCP now guarantees that AI models only process information contextually relevant to their tasks and operate within predefined security parameters, mitigating risks such as hallucinations. * This development enables the secure deployment of sophisticated AI tools, such as ChatGPT, across digital banking platforms for various applications. * Narmi developed these expanded MCP features, allowing its banking clients to leverage the power of AI more effectively and safely within financial services.

The article introduces and compares two significant protocols in the AI assistant landscape: the Model Context Protocol (MCP) and the emerging Agent-to-Agent (A2A) protocol. * MCP, developed by Anthropic, enables AI assistants like Claude to access and utilize external tools and resources, effectively allowing the model to interact with the outside world. * It defines a structured way, often using JSON, for AI clients to communicate with 'MCP Servers' for tasks such as web searching, executing code, or interacting with APIs. * A2A is presented as a complementary protocol focused on facilitating direct communication and collaboration between different AI agents. * The article highlights that while MCP focuses on AI-to-Tool interaction, A2A targets AI-to-AI interaction, both contributing to more complex and agentic AI workflows.

The Model Context Protocol (MCP) is identified as a critical foundational technology for the evolution of the marketing stack, empowering AI agents to interact with the broader digital ecosystem. * MCP offers a standardized framework, likened to an 'API for APIs,' enabling AI agents to dynamically access and utilize external tools and APIs beyond their internal data. * This protocol allows AI agents to perform complex, multi-step tasks by breaking out of their 'sandbox' and learning from real-world interactions with various marketing platforms. * MCP is crucial for overcoming the 'context window problem' by enabling agents to fetch and process specific, relevant information on demand for ongoing tasks. * Its implementation is expected to drive the development of more autonomous and capable AI agents that can manage and optimize diverse marketing operations.

The article provides a detailed guide on setting up an MCP Server to enable AI assistants to interact with external tools and automation workflows. It demonstrates how to configure and run an MCP Server that facilitates Claude's ability to generate and apply Terraform code for infrastructure management. GitHub Actions are integrated to automate the execution of Terraform changes triggered through the MCP Server. The tutorial covers deployment of the MCP Server, defining tool configurations, and connecting Claude as the client to invoke these external capabilities.

Trail of Bits introduced Slither MCP, a new tool integrating their Slither static analysis framework with the Model Context Protocol (MCP). * Slither MCP converts detailed Solidity analysis results from Slither into MCP-compatible formats. * The tool provides Large Language Models (LLMs) and AI assistants with structured, high-quality context for Solidity codebases. * It enables AI agents to perform advanced security analysis, explain vulnerabilities, suggest fixes, and reason about smart contract logic more effectively. * Slither MCP enhances the capabilities of developer-focused AI tools working within the blockchain and smart contract development ecosystem.

Microsoft has integrated an Azure Model Context Protocol (MCP) server directly into Visual Studio 2026. * The built-in MCP server is designed to streamline agentic workflows, enabling AI assistants to access tools and manage context seamlessly within the development environment. * This integration aims to simplify the creation and deployment of sophisticated AI agents by providing standardized mechanisms for external resource interaction. * Developers can leverage Visual Studio 2026 to build AI assistants that efficiently interact with codebases and external services via the MCP. * This development marks a significant advancement in tooling for AI-driven automation and intelligent application development.