Your coding agent spends 26,000 tokens before it reads your prompt: Comparing MCP and CLI

AI coding-tool adoption has grown substantially, prompting debate over whether Model Context Protocol (MCP) servers or command-line interfaces (CLIs) use model context more efficiently. The comparison highlights how large MCP tool schemas can consume tens of thousands of tokens before work begins, while CLI commands can reveal only the specific help or functionality needed, although the text argues that this is chiefly an architectural issue of eager versus on-demand tool disclosure rather than an inherent protocol flaw. Lean MCP implementations, hierarchical discovery, and code-execution approaches can sharply reduce schema overhead, while CLIs retain advantages in Unix-style composition and familiarity from training data. MCP, meanwhile, can provide more controlled credential handling and governance when agents access external systems, despite not eliminating prompt-injection risks or always covering full APIs. The author’s Blocks.ai measurements find no initialization overhead for its CLI, 3,185 tokens for its 15-tool MCP server, 12,755 tokens for its skill file, and up to 26,644 tokens for a fully enabled GitHub MCP server. Based on these results, the piece recommends using CLIs for local agent-building workflows, MCP for invoking existing agents through a stable small toolset, and skill files for conveying conventions and operational knowledge.

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