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Every turn your AI agent runs, it pays a hidden token tax — every tool schema gets injected into the context window, whether the model uses it or not. With 30+ tools, that's thousands of wasted tokens per turn. mcp2cli is an open source CLI tool that eliminates this problem. Instead of injecting schemas, it lets agents discover tools on-demand through a lightweight command-line interface. The result: 96-99% token savings on tool overhead, with support for MCP servers, OpenAPI specs, and GraphQL endpoints. In this video I walk through exactly how mcp2cli works under the hood, trace through its four modes (OpenAPI, MCP, GraphQL, and Bake), cover the production-ready details like OAuth, caching, and secret management, and give you a complete setup guide from install to AI agent integration. Note: This video is generated by AI. I designed a workflow that uses OpenCode to investigate open-source repositories to write a script about public codebases. Some of the information in this video may be inaccurate. If you maintain one of the projects I cover and notice a discrepancy with what the video says and what the codebase is, please leave a comment and I will investigate. 0:00 Intro 1:25 The token tax problem 2:55 How mcp2cli solves it 4:51 Code walkthrough & architecture 6:36 OpenAPI mode 7:53 MCP mode 8:52 GraphQL mode 10:22 Production-ready details (OAuth, caching, sessions) 13:16 Step-by-step setup guide 15:55 FAQ — latency, LLM compatibility, MCP vs mcp2cli 18:41 Wrap up mcp2cli on GitHub: https://github.com/knowsuchagency/mcp...