I think part of the problem is how these mcp service are designed. A lot of them just returns Mbs of text blob without filtering at all, and thus explodes the context.

And it's also affected by how model is trained. Gemini specifically like to read large amount of text data directly and explodes the context. But claude try to use tool for partial search or write a script to sample from a very large file. Gemini always fills the context way faster then claude when doing the same job.

But I guess in case of a bad designed mcp, there is no much model can do because the results are injected into context directly though (unless the runtime decided to redirect it to somewhere else)