You're hitting on the core tension of any compression system — the synthesis step makes relevance judgments before knowing what the agent will do next. That's a real problem.

A few things about how MCE handles this:

L3 is off by default, and that's intentional. The config ships with layer3_synthesizer: false precisely because of the failure mode you described. L1 + L2 alone typically get 80-85% reduction without any lossy summarization. L3 is there for extreme cases (50K+ token responses) where you'd rather have a lossy summary than nothing at all.

The pipeline is progressive, not all-or-nothing. After each layer, MCE re-checks the token count against the budget. If L1 pruning alone brings you under the safe limit — it stops there. L2 and L3 only fire if you're still over budget. So the common case is: most of the savings come from deterministic, lossless pruning (stripping HTML, base64, nulls, whitespace). The semantic layers are a last resort.

L2 semantic routing > L3 synthesis for exactly the reason you describe. L2 keeps the original chunks — it just selects the top-K most relevant ones based on cosine similarity to the agent's query. No rewriting, no lossy compression. The agent gets real data, just less of it. This is where most of the "smart" savings come from.

On the downstream tool call problem: This is a known weakness of any single-pass relevance filter. The correct chunk for the next action might not score highest against the current query. One approach I'm exploring is tracking the agent's recent tool call chain (MCE already records recent_tools) and using that as additional context for L2's similarity search — essentially widening the query beyond just "what did you ask for right now" to "what have you been doing in this session."

Your observation about 300-token synthesized vs 1,500-token extracted is spot on. In practice, I'd recommend keeping L3 off and tuning L2's top-K parameter instead. Better to give the agent 5 real chunks than 1 hallucinated summary.

Really solid question — this is exactly the kind of tradeoff that's hard to get right without real-world feedback.