So much incorrect and misinformation in these comments. As someone who is building an agent[0] with MCP tools, neither the MCP tool description nor the response is the problem. Both of those are easily solved by not bloating them.

The real killer is the input tokens on each step. If you have 100k tokens in the conversation, and the LLM calls an MCP tool, the output and the existing conversation is sent back. So now you've input 200k tokens to the LLM.

Now imagine 10 tool calls per user message - or 50. You're sending 1-5M input tokens, not because the MCP definitions or tool responses are large, but because at each step, you have to send the whole conversation again.

"what about caching" - Only 90% savings, also cache misses are surprisingly common (we see as low as 40% cache hit rate)

"MCP definitions are still large" - not compared to any normal conversation. Also these get cached

We've seen the biggest savings by batching/parallelizing tool calls. I suspect the future of LLM tool usage will have a different architecture, but CLI doesn't solve the problems either.

[0] https://ziva.sh, it's an agent specialized for Godot[1]

[1] https://godotengine.org