amber_search_memories
Find active memories by asking natural-language questions. Results ranked by relevance, with optional metadata and topic filtering.
Instructions
Find active memories by semantic meaning. Write the query as a natural-language question or description, NOT as keywords. Good: "What are the user's dietary preferences?", "meetings the user had last week". Bad: "diet food preferences", "meeting notes". The query is automatically expanded with synonyms and related terms to improve recall. Supports optional metadata filtering (e.g. { "user_tag": "work" }). Results are ordered by relevance (higher score = better match). Scores are only meaningful for ranking within a single query, not across different queries. Use amber_list_memories for chronological browsing. Optional topics param: pass topic names (like when storing) to filter by topic — matched semantically, so "beliefs" also finds memories tagged with "opinions" or "superstitions". Results from closely matching topics rank higher than fuzzy matches. Content is truncated to 1000 chars in search results — use amber_get_memory for full content. Rate-limited (drip bucket: 5000 capacity, refills ~1 token per 17 seconds).
Input Schema
| Name | Required | Description | Default |
|---|---|---|---|
| query | Yes | Natural-language question or description of what to find. Use full sentences, not keywords. | |
| n_results | No | Maximum number of results to return (default 10, max 100). | |
| metadata_filter | No | Optional metadata filter. Matches memories whose metadata contains every key-value pair given. | |
| topics | No | Optional topic filter. Pass category names like 'food', 'work', 'beliefs'. Matched semantically — 'beliefs' also finds memories categorized under related topics like 'opinions' or 'superstitions'. |