This topic covers frameworks, servers, and deployment patterns that use the Model Context Protocol (MCP) to connect large language models and multi-agent kernels to real-world tools, data, and execution environments. MCP servers expose capabilities—database access, browser automation, code execution, repository actions—so agents can call tools as composable, networked services. Key examples include Daytona for running AI‑generated code in isolated, elastic sandboxes; Browser MCP / Agent TARS for a multimodal agent kernel that mounts MCP servers across terminals, desktops and browsers; GitHub’s MCP Server for secure repository and issue interactions; Playwright MCP Server for browser automation; pydantic’s mcp-run-python for sandboxed Python execution (Deno/Pyodide); Kiln’s MCP integrations for orchestrating external tools from task flows; and the MCP Toolbox for Databases to handle pooling, credentials and connection complexity. This area is timely as of 2026 because production agent deployments increasingly require standardized tool interfaces, strong runtime isolation, and cloud-native scalability. Kubernetes and microservice patterns are common for running MCP servers as sidecars or services behind ingress and service meshes to provide lifecycle management, horizontal scaling, observability and policy controls. Practical concerns shaping adoption include sandbox security, authentication and authorization between agents and servers, connection pooling for high-throughput database or browser workloads, A2A (agent-to-agent) communication, and predictable resource limits for safe execution of generated code. Evaluations in this space focus on interoperability, security posture, operational overhead, and how easily MCP servers integrate into CI/CD and Kubernetes tooling. The result is an ecosystem where standardized MCP servers enable modular, auditable, and scalable agent orchestration without embedding ad‑hoc capabilities directly into LLM prompts or single-host processes.