# CLIs trade tool discoverability for token efficiency For an AI agent, a CLI consumes far fewer context tokens than a loaded MCP schema because the agent discovers commands progressively (`--help`, subcommands) and pays only for what it inspects — but it must already know the commands exist. MCP front-loads discoverability (structured, enumerable tool definitions) at a token cost. The efficiency gap is "closing in on each other" as MCP implementations get leaner. - Scope: AI agent tool interfaces (observability tooling case). - Pointer: Grafana GCX vs MCP server comparison. <!-- atomicity: 1 idea ✓ --> ## Links - [[3. Resources/Literature Notes/Lit · MCP vs CLI tradeoffs for AI agents]] — evidence-for; the source directly compares lower CLI context cost with MCP's stronger upfront tool discovery. - [[3. Resources/Permanent Notes/Tool-Interface Choice Shapes Agent Architecture More Than Model Choice]] — evidence-for: CLI discovery defers schema cost until the agent asks for help, trading immediate enumeration for lower context usage - [[3. Resources/Permanent Notes/Vague Prompts Are Expensive Specificity Cuts AI Error and Cost Together]] — extends: progressive command discovery and request specificity both reduce tokens spent on irrelevant possibilities - [[3. Resources/MOCs/AI Cost & Tokenomics MOC]] — applies-in: the MOC lists this note under Synthesis additions (2026-06-11). ## Source - [[3. Resources/Readwise/Articles/CLI and MCP Basics for AI Agents|CLI and MCP Basics for AI Agents — The Context Window #4]]