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Find memories ranked by relevance to a query. Hybrid semantic and keyword search with reranking returns scored records with IDs for precise retrieval.

Instructions

Find individual memories ranked by relevance to a query. Semantic-primary when embedding models are loaded, falling back to deterministic BM25/FTS keyword search otherwise; cross-encoder reranked by default. Read-only. Returns scored, individual memory records (with ids) — use this to locate or inspect specific memories. To assemble a prompt-ready context block, use pack instead; to browse recent memories without a query, use memory_list.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
tagsNoRestrict to memories carrying these tags.
limitNoMaximum number of memories to return. Default 10.
queryYesNatural-language search query. Required.
spaceNoRestrict to a single memory space (namespace). Omit to search the default space.
rerankNoApply the cross-encoder reranker to the candidate pool. Default true.
entity_keyNoRestrict to memories linked to this entity key.
include_sourceNoIf true, reveal provenance/source metadata. Default false.
include_contentNoIf true, return each memory's full text instead of a snippet. Default false.
semantic_enabledNoForce semantic retrieval on or off. Default: on when embedding models are available, else lexical.
Behavior5/5

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

Discloses read-only nature, fallback from semantic to BM25/FTS when embedding models unavailable, and default cross-encoder reranking. No annotations provided, so description carries full burden and does so thoroughly.

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?

Concise multi-sentence description with front-loaded purpose. Every sentence adds value; no wasted words.

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?

No output schema, but description explains return type (scored records with ids) adequately. Covers behavioral aspects, usage guidance, and parameter options. Complete for a search tool with 9 parameters.

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 coverage is 100%, so baseline is 3. Description adds minimal value beyond schema; mentions 'scored' records but no new semantics for individual parameters.

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?

Clearly states it finds individual memories ranked by relevance. Distinguishes from siblings 'pack' and 'memory_list' by specifying their different purposes.

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?

Explicitly advises when to use alternatives: 'pack' for context blocks, 'memory_list' for browsing without a query. Also explains fallback behavior between semantic and lexical search.

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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