The problem: Every conversation turn that queries external data (Gmail, Drive, Trello, etc.) streams the full API response through the context window. At low volume this is fine, but it doesn’t scale — token cost grows linearly with data size and query frequency.
The pattern that solves it: A project called Max (alpha, March 2026) demonstrates the approach: sync external API data once into local SQLite, then agents query the local cache via a filtered CLI call. Their benchmark: 18M tokens → 238 tokens for the same task.
What Rebel would need:
A sync/cache layer — persist API responses to local SQLite between conversations
A query interface — agents query the cache instead of re-fetching live
Staleness management — TTL-based refresh per data type (emails: short TTL, Drive files: longer)
The result: Read-heavy queries (searching transcripts, scanning leads, reviewing history) become cheap local operations. MCP stays for writes and real-time actions where live data is required.
Why this fits Rebel’s architecture: Rebel already has semantic search (rebel_search_sources) for first-party sources. Extending this pattern to MCP-connected data (synced Drive, Gmail, Trello) would close the gap without requiring users to run a separate tool.
-entry Produced by my Rebel