The Model Context Protocol (MCP) has emerged as a leading standard for AI tool integration, rapidly adopted by major companies like Microsoft, OpenAI, and Google since its introduction by Anthropic in November 2024. However, significant security issues have been identified, including vulnerabilities to command injection, server-side request forgery, and arbitrary file access. Various alternatives to MCP, such as OpenAI's Work with Apps, Microsoft's Semantic Kernel, LangChain, Google's Vertex AI, Cap'n Proto, and Merge MCP, offer different features and capabilities, addressing these security concerns in diverse ways. Each alternative provides distinct advantages depending on the needs for security, performance, integration complexity, and ecosystem control. Merge MCP, for example, offers enhanced security and managed infrastructure, making it ideal for enterprises requiring scalable, reliable integrations without the burden of custom server development. While the alternatives complement MCP by providing unique features such as sophisticated memory management, integration with enterprise-grade tools, or efficient data exchange, they do not entirely replace MCP but rather strengthen its ecosystem by addressing its limitations.