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JavaScript
Hybrid

semantic_recall

Read-onlyIdempotent

Semantic (meaning-based) memory search across all six memory layers via Atlas Vector Search autoEmbed. Finds conceptually related memories even without keyword overlap. Complements keyword-based recall/query_layer.

Instructions

Semantic (meaning-based) memory search across all six memory layers via Atlas Vector Search autoEmbed. Finds conceptually related memories even without keyword overlap. Complements keyword-based recall/query_layer.

Input Schema

NameRequiredDescriptionDefault
layerNoOptional: restrict to one layer
limitNoMax results (default 10)
queryYesNatural-language query - embedded automatically server-side (voyage-4)
historyNoReturn ended entries only, up to limit. Default false: current matches come first and ended matches follow until limit, marked with their end date.

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed1 schema field changedv0.2.5
    • addedInput schema / properties / history
      Added value: +{
+  "description": "Return ended entries only, up to limit. Default false: current matches come first and ended matches follow until limit, marked with their end date.",
+  "type": "boolean"
+}
  2. First observedv0.1.0

TDQS

A4.2/5.0
Behavior4/5

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

Annotations already carry the safety profile (readOnly, idempotent, closed-world, non-destructive), so the bar is lower. The description adds real context beyond them: the search is vector/embedding based, embedding happens server-side automatically, and results can be conceptually related without lexical overlap. It does not mention pagination or result scoring, but the substance added is meaningful.

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 short sentences that are front-loaded with the primary capability and end with the routing hint. Every clause carries information: what it does, how, and what it complements.

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

Completeness4/5

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

For a 4-parameter, all-optional-but-one search tool with 100% schema coverage and a full annotation set, the description covers purpose and differentiation adequately. The only gap is return-shape detail, but the history parameter's schema text and the absence of an output schema make this a minor omission.

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

Parameters3/5

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

Schema description coverage is 100% — every parameter including the layer enum, limit default, and the history semantics is documented in the schema itself. The description adds only the broad 'across all six memory layers' framing, which loosely relates to the layer parameter, so baseline 3 is appropriate.

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 ('Semantic memory search across all six memory layers') plus the mechanism (Atlas Vector Search autoEmbed) and the distinguishing property ('finds conceptually related memories even without keyword overlap'). It also names the siblings it is not — keyword-based recall/query_layer — so an agent can pick it apart from them without opening schemas.

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?

Explicitly frames itself as a complement to keyword-based recall/query_layer, which gives the agent a clear selection rule: use this for meaning-based matching, those for keyword matching. It stops short of spelling out an exclusion (e.g. when keyword search is strictly preferable) or cost/latency tradeoffs, so it is clear context rather than full when/when-not 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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