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vault-memory

by owrede
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TypeScript
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search_hybrid

Read-onlyIdempotent

Search vault notes by combining semantic and BM25 full-text results with Reciprocal Rank Fusion, and optionally expand hits with typed-edge neighbors for context.

Instructions

Hybrid search: combines semantic (embedding) and BM25 (full-text) results via Reciprocal Rank Fusion. Best general-purpose query. Pass expand: {hops: 1} to auto-attach 1–2 hop typed-edge neighbors as expansions[] per hit (preserves ranking; runs after recency/authority rescore).

Input Schema

NameRequiredDescriptionDefault
queryYes
rrf_kNo
top_kNo
expandNo
rerankNo
vaultsNo
exclude_pathsNo
half_life_daysNo
recency_weightNo
authority_weightNo
frontmatter_boostsNo
frontmatter_filterNo
include_supersededNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed2 schema fields changedv2.4.1
    • addedInput schema / properties / frontmatter_boosts
      Added value: +{
+  "items": {
+    "properties": {
+      "key": {
+        "minLength": 1,
+        "type": "string"
+      },
+      "value": {
+        "type": "string"
+      },
+      "weight": {
+        "type": "number"
+      }
+    },
+    "required": [
+      "key",
+      "value",
+      "weight"
+    ],
+    "type": "object"
+  },
+  "type": "array"
+}
    • addedInput schema / properties / frontmatter_filter
      Added value: +{
+  "items": {
+    "properties": {
+      "key": {
+        "minLength": 1,
+        "type": "string"
+      },
+      "value": {
+        "type": "string"
+      }
+    },
+    "required": [
+      "key",
+      "value"
+    ],
+    "type": "object"
+  },
+  "type": "array"
+}
  2. First observedv2.0.0-rc.5

TDQS

A3.9/5.0
Behavior4/5

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

Annotations already cover the safety profile (readOnly, idempotent, non-destructive, closed-world), so the bar is lower. The description adds real behavioral detail: RRF fusion, that expansions preserve ranking and run after recency/authority rescore. However, it leaves the behavior of ~11 tuning knobs (rerank, recency_weight, authority_weight, half_life_days, include_superseded, frontmatter filters) undisclosed.

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

Conciseness5/5

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

Three dense, front-loaded sentences with zero filler: mechanism first, positioning second, the one parameter worth elaborating last. Every clause earns its place.

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

Completeness2/5

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

With 13 parameters, a nested `expand` object, 0% schema description coverage, and no output schema, the description is far too thin to let an agent configure the tool confidently. It documents the optional expansion path well but omits any hint about scoring, filtering, or supersession parameters that materially change results.

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

Parameters2/5

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

Schema description coverage is 0% across 13 parameters, so the description must carry the parameter burden and it only explains one: `expand` (hops 1–2, expansions[] per hit, ordering). The other twelve parameters — rrf_k, top_k, rerank, half_life_days, recency_weight, authority_weight, frontmatter_boosts/filter, include_superseded, vaults, exclude_paths — get no semantic explanation at all.

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?

States a specific verb+resource ('Hybrid search') and names the exact mechanism (semantic/embedding + BM25 fused via RRF), which immediately distinguishes it from the sibling search_semantic and search_text tools. The claim 'Best general-purpose query' further positions it relative to alternatives.

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

Usage Guidelines4/5

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

'Best general-purpose query' gives clear positive guidance on when to reach for this tool over the specialized siblings. It lacks an explicit when-not or a named alternative for narrow cases (e.g. when to use search_text instead), so it stops short of full routing guidance.

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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