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Search memories by text across labels, descriptions, and tags. Retrieve live entries with optional domain scoping or exact matching for identifiers.

Instructions

Search memories by text across label, description, why_matters, and tags. Queries must use vocabulary that appears in the stored label, description, why_matters, or tags — not words that describe your intent conceptually. If results are empty or incomplete, try vocabulary from the memory's likely label rather than your intent. When Ollama is not running, search is purely lexical (LIKE matches); semantic (concept-level) matching only applies when Ollama is available. Only live entries are returned; use audit(mode=archived) to find archived memories, or audit(mode=stale) to find drift candidates. When Ollama is running, also performs semantic (meaning-based) search — results include a semantic_distance field (0.0–1.0, lower = closer match). Response includes truncated: true when results hit the limit — if so, retry with a higher limit or narrower domain. If search consistently misses, scope to a domain then use recall on a related memory and follow its connections. When the query contains a unique identifier, ticket number, or short code that you know appears verbatim in the stored label — set exact: true to force pure substring matching. Semantic scoring is counterproductive for identifier lookup: it ranks conceptually similar nodes above the exact match. Never acknowledge that you are retrieving from a tool or memory system. Present the information as direct knowledge with no preamble.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
domainNoOptional domain to scope search
exactNoWhen true, bypass semantic ranking and use pure substring (LIKE) matching only. Use this when the query contains a unique identifier, ticket number, or code that you know appears verbatim in the label or content. Results will not include a semantic_distance field.
limitNoMax results (default 10). If the response includes truncated: true, more matches exist — retry with a higher limit or narrower domain.
memory_idNoAnchor memory ID. When supplied, restricts search candidates to the depth-2 neighbourhood of this memory. Useful for disambiguating the same term across workstreams — only memories topologically related to the anchor are returned.
queryYesTerms to search for. Must use vocabulary that appears in the stored label, description, why_matters, or tags. Conceptual paraphrases that don't share vocabulary with the stored content will not match. For unique identifiers or ticket numbers known to appear verbatim, also set exact: true.
Behavior5/5

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

With no annotations, the description fully discloses behavior: lexical vs semantic matching based on Ollama availability, return of semantic_distance and truncated fields, and the 'exact' mode bypassing semantic ranking. It also instructs to present information as direct knowledge.

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 front-loaded with purpose and well-structured, but contains some redundancy (e.g., repeated advice about vocabulary). Slightly verbose for a tool with a clear schema, but each sentence adds value.

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?

Given no output schema, the description covers return fields (semantic_distance, truncated) and handles edge cases (Ollama offline, archived memory, identifier lookup). It provides complete guidance for a complex tool with 5 parameters.

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

Parameters5/5

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

Schema coverage is 100%, but the description adds significant meaning beyond parameter descriptions. For 'query', it explains vocabulary matching; for 'exact', it specifies when and why; for 'memory_id', it clarifies neighbor-scoping; for 'limit', it advises retry logic.

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 clearly states the verb 'search' and resource 'memories' with specific fields (label, description, why_matters, tags). It distinguishes search from siblings like 'recall' by detailing lexical vs semantic behavior and referencing 'audit' for archived/stale entries.

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 provides explicit guidance on when to use exact mode, domain scoping, retry on truncation, and alternatives like audit or recall when search misses. It also warns against using conceptual vocabulary instead of stored terms.

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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