mcp_engram_query_with_momentum
Retrieve concepts that are actively changing or evolving by blending semantic similarity with conceptual trajectory, ideal for tracking trends or shifts in a topic.
Instructions
Momentum-assisted recall: blends semantic similarity (q tensor, 80%) with conceptual trajectory (p tensor, 20%). WHEN TO USE INSTEAD OF recall: When you want to find concepts that are actively changing or evolving, not just ones that statically match your query right now. Example: use this when asking 'what has been changing in the auth system?' because momentum detects blocks whose p tensor is accelerating toward your query topic. Use regular recall when you want stable, crystallized knowledge. Supports zedos_filter (incl. 'training' for Phase 2 NREM-biased richer CLS blocks).
Input Schema
TableJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
| k | No | Number of results to return (default: 5, max: 20) | |
| query | Yes | Natural language query | |
| zedos_filter | No | Optional: filter by memory type (same values as mcp_engram_recall, including 'training' for ZEDOS_TRAINING / richer CLS blocks). Leave unset for all types. |