Nice one.

You should also try to make context query the first class primitive.

Context query parameter can be natural language instruction how to compact current context passed to subagent.

When invoking you can use values like "empty" (nothing, start fresh), "summary" (summarizes), "relevant information from web designer PoV" (specific one, extract what's relevant), "bullet points about X" etc.

This way LLM can decide what's relevant, express it tersly and compaction itself will not clutter current context – it'll be handled by compaction subagent in isolation and discarded on completion.

What makes it first class is the fact that it has to be built in tool that has access to context (client itself), ie. it can't be implemented by isolated MCP because you want to avoid rendering context as input parameter during tool call, you just want short query.

Ie. you could add something like:

  handover(prompt, context_query, depends_on: { conversation_id_1: "result", conversation_id_2: "just result number" }) -> conversation_id"

depends_on is also based on context query but in this case it's a map where keys are subagent conversation ids that are blockers to perform this handed over task and value is context query what to extract to inject.