DockaShell is an MCP (Model Context Protocol) server that gives AI agents isolated Docker containers to work in. Each agent gets its own persistent environment with shell access, file operations, and full audit trails. It aims to remove limitations of current AI assistants like lack of persistent memory, tool babysitting, limited toolsets, and no self-reflection, enabling self-evolving agents, continuous memory, autonomous exploration, and meta-learning.
This article details significant security vulnerabilities found in the Model Context Protocol (MCP) ecosystem, a standardized interface for AI agents. It outlines six critical attack vectors – OAuth vulnerabilities, command injection, unrestricted network access, file system exposure, tool poisoning, and secret exposure – and explains how Docker MCP Toolkit provides enterprise-grade protection against these threats.
Docker introduces the enhanced MCP Catalog, offering secure discovery and execution of MCP servers, addressing security concerns with containerized solutions, and opening the submission process to the community.
This blog details how to use Docker MCP Catalog and Docker MCP Toolkit to easily spin up and manage Model Context Protocol (MCP) servers, connecting them to clients like Claude and Cursor.
Docker is making it easier for developers to run and test AI Large Language Models (LLMs) on their PCs with the launch of Docker Model Runner, a new beta feature in Docker Desktop 4.40 for Apple silicon-powered Macs. It also integrates the Model Context Protocol (MCP) for streamlined connections between AI agents and data sources.
