Memory Recall
vault_memory_recallRecall memory entries on any topic across all vault memory files and dates, using hybrid keyword and semantic search to reveal how a preference, opinion, or fact evolved.
Instructions
Recall memory entries about a topic — entry-granular hybrid (keyword + semantic) retrieval across ALL About Me/ files and ALL time. Returns every relevant dated entry sorted oldest-first, so the full evolution of a preference, opinion, or fact is visible — semantic matching finds early entries even when their phrasing differs from the query. Tuned for recall over precision: expect some marginal entries and judge relevance yourself when synthesizing an answer. Content-word queries ("testing philosophy", "sustainable pacing") rank best. A meta-framed query ("opinions on testing") that scores below the relevance threshold degrades to relaxed any-term keyword matching instead of returning nothing.
Example: vault_memory_recall({ query: "working hours and pacing" }) Example: vault_memory_recall({ query: "testing philosophy", file: "Opinions" })
When to use: Answering "what does my memory say about X?" or "how has my view on Y evolved?" — topic-based recall across memory files. Prefer vault_get_memory to read a known file or section verbatim; prefer vault_search for notes outside the memory layer.
Errors:
No matching entries returns { entries: [], total: 0 }, not an error
An unknown file returns empty results — call vault_list_memory_files to discover valid names
Returns: JSON { entries, total, truncated, search_mode, reranked }. Each entry is { file, section, date, text } — text is the raw entry markdown (wikilinks intact, continuation lines included); file and section feed directly into vault_get_memory or vault_delete_memory. entries ascend by date (oldest first). total counts all matched entries; truncated=true means limit dropped the least-relevant matches — never a date range — so raise limit or narrow the query for the complete set. file is applied before limit, so limit keeps the most relevant matches from that file. search_mode is "hybrid" when vector matching contributed, "fts" when the entries came from keyword matching alone — including the any-term fallback that rescues a would-be-empty result; reranked is true when the cross-encoder relevance cut was applied.
Input Schema
| Name | Required | Description | Default |
|---|---|---|---|
| file | No | Optional: restrict to one memory file, name without .md (e.g. "Opinions"), in its exact letter case. Omit for cross-file recall — the default and usual choice. | |
| limit | No | Cap on returned entries (default 50). | |
| query | Yes | Topic to recall — natural language works best (semantic matching bridges phrasing drift across months); content words about the topic rank better than meta framing ("testing philosophy" over "opinions on testing") |