Show HN: Mcp2cli – One CLI for every API, 96-99% fewer tokens than native MCP

It's about letting LLM Agents use the CLI, the end user still interfaces with the agent via chat.

Yes, MCP could've been solved differently - eg with an extension to the openapi spec for example, at least from the perspective of REST APIs... But you're misunderstanding the selling point.

The issue is that granting the LLM access to the API needs something more granular then "I don't care, just keep doing whatever you wanna do" and getting promoted every 2 seconds for the LLM to ask the permission to access something.

With MCP, each of these actions is exposed as a tool and can be safely added to the "you may execute this as often as you want" list, and you'll never need to worry that the LLM randomly decides to delete something - because you'll still get a prompt for that, as that hasn't been whitelisted.

This is once again solvable in different ways, and you could argue the current way is actually pretty suboptimal too... Because I don't really need the LLM to ask for permission to delete something it just created for example. But the MCP would only let me whitelist action, hence still unnecessary security prompts. But the MCP tool adds a different layer - we can both use it as a layer to essentially remove the authentication on the API you want the LLM to be able to call and greenlight actions for it to execute unattended.

Again, it's not a silver bullet and I'm sure what we'll eventually settle on will be something different - however as of today, MCP servers provide value to the LLM stack. Even if this value may be provided even better differently, current alternative all come with different trade-offs

And all of what I wrote ignores the fact that not every MCP is just for rest APIs. Local permissions need to be solved too. The tool use model is leaky, but better then nothing.

The lazy discovery pattern is clever. Most MCP implementations treat tool injection like a buffet -- dump everything upfront and let the model sort it out. The problem is you pay the context cost whether the model uses those tools or not, and at 120+ tools that cost compounds fast.

The analogy I'd draw is database query planning: you don't load the entire schema into memory before every query, you resolve references on demand. Same principle here. Does the CLI maintain a tool cache between invocations, or does it re-fetch schemas each time?

It caches the spec with a default TTL of 1 hour

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