Harnessing AI, particularly Large Language Models (LLMs), is an attractive yet costly endeavor, making cost management essential for sustainable use. Strategies like Anthropic's Model Context Protocol (MCP) help in optimizing token efficiency, crucial for cost management and performance enhancement. Tokens, the fundamental units LLMs use to process text, contribute significantly to costs, as pricing structures often depend on token usage. MCP servers, serving as smart interfaces between LLMs and external resources, can improve an LLM's efficiency by managing the flow of necessary information, thereby reducing token usage. Effective MCP server implementation involves understanding the context window, optimizing API interactions, and managing the volume of data supplied to the LLM. Developers have learned that optimizing the server setup and being judicious with the data and tools provided can enhance performance and reduce costs. Additionally, best practices such as using caching techniques and LLM observability tools can further mitigate token usage and improve overall system efficiency.