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Vault Cortex Obsidian MCP Server

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TypeScript
Hybrid

Memory Recall

vault_memory_recall
Read-onlyIdempotent

Recall topic-based memory entries across all memory files, sorted oldest-first to show how views evolved. Hybrid keyword and semantic search finds relevant entries even when phrasing differs.

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") whose relevance cut would come back empty 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: "opinions on testing", 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. 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

TableJSON Schema
NameRequiredDescriptionDefault
fileNoOptional: restrict to one memory file, name without .md (e.g. "Opinions"). Omit for cross-file recall — the default and usual choice.
limitNoCap on returned entries (default 50). When more match, the least-relevant are dropped and truncated=true — never a date range.
queryYesTopic 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")

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed2 schema fields changedv0.50.0
    • addedInput schema / properties / limit
      Added value: +{
+  "default": 50,
+  "description": "Cap on returned entries (default 50). When more match, the least-relevant are dropped and truncated=true — never a date range.",
+  "maximum": 9007199254740991,
+  "minimum": 1,
+  "type": "integer"
+}
    • removedInput schema / properties / max_results
      Removed value: -{
-  "description": "Cap on returned entries (default 50). When more match, the least-relevant are dropped and truncated=true — never a date range.",
-  "type": "number"
-}
  2. Addedv0.32.1
  3. Removedv0.32.0
  4. Addedv0.27.2

TDQS

A4.8/5.0
Behavior5/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already declare readOnlyHint=true, idempotentHint=true, destructiveHint=false, so the safety profile is covered. The description adds substantial behavioral context beyond that: the recall-over-precision tuning, the fallback to relaxed any-term keyword matching when a meta-framed query would return empty, the oldest-first date ordering, the meaning of truncated (drops least-relevant matches, never a date range), and the search_mode/reranked fields. This is rich, non-obvious behavior that an agent needs to interpret results correctly.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is long but every section earns its place: purpose, query guidance, examples, when-to-use, error behavior, and return semantics. It is front-loaded with the core purpose and scoping, then moves to usage guidance and edge cases. It could be slightly tightened, but the density of useful information justifies the length.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a retrieval tool with no output schema, the description fully compensates by documenting the return shape ({ entries, total, truncated, search_mode, reranked }), the entry fields ({ file, section, date, text }), the ordering, the truncation semantics, and the error behavior (empty result, not an error). It also covers the main failure mode (unknown file) and how to recover. Nothing an agent needs to call it correctly and interpret results is missing.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, so the schema already documents all three parameters. The description adds value by explaining the query semantics in depth (content words vs meta framing, semantic matching bridging phrasing drift) and by clarifying the file parameter's default behavior (omit for cross-file recall). The limit parameter's truncation behavior is also explained in the description. This goes beyond the schema's baseline, though the schema already carries the core meaning.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description states a specific verb ('Recall'), a specific resource ('memory entries about a topic'), and the retrieval mechanism ('entry-granular hybrid keyword + semantic retrieval across ALL About Me/ files and ALL time'). It clearly distinguishes itself from siblings like vault_get_memory and vault_search by naming them and the conditions for preferring them. The example calls further anchor the tool's purpose.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description explicitly says when to use this tool ('Answering what does my memory say about X? or how has my view on Y evolved?') and when not to ('Prefer vault_get_memory to read a known file or section verbatim; prefer vault_search for notes outside the memory layer'). It also provides error-handling guidance (unknown file returns empty results, call vault_list_memory_files) and query-shaping advice (content-word queries rank best).

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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