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Search memories by text across labels, descriptions, and tags. Omit domain to find results across all domains or narrow by a single domain for better precision.

Instructions

Search memories by text across label, description, why_matters, and tags. Omitting domain searches the entire workspace across all domains. Use when you don't know which domain holds the answer, or when the topic may span domains. Scope to a single domain when you know it — results are cleaner and faster. 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: (0) try search without domain if the target domain is unknown; (1) scope to a domain; (2) recall on a related memory and follow its connections; (3) orient. 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. Returns lean node data only — id, label, and a short excerpt. If you need full node content, call recall(id). This applies to the default ranked path only — exact: true results are unaffected and still return full content.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
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.
queryNoTerms to search for. Must use vocabulary that appears in the stored label, description, why_matters, or tags. Required unless node_kind is set alone (lists by kind). For unique identifiers known to appear verbatim, also set exact: true.
digestNoWhen true, collapse each result memory to a single compact text line in a lines array instead of JSON objects — saves tokens on multi-result calls. Default false. Does not apply when exact: true (full content path). Each line includes id for recall(id) follow-up.
domainNoOptional domain to scope search. Omit to search the entire workspace across all domains. Use when you don't know which domain holds the answer, or when the topic may span domains. Scope to a single domain when you know it — results are cleaner and faster.
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.
node_kindNoOptional filter by node_kind. Space-separated for OR match (e.g. 'decision standing'). When set without query, lists matching memories ordered by updated_at DESC.
Behavior5/5

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

No annotations are present, so the description carries the full burden. It discloses that search is lexical when Ollama is not running, semantic when available, and that results include semantic_distance field. It also explains that exact:true bypasses semantic ranking and that truncated:true indicates max results hit. All behavioral traits are clearly documented.

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 front-loaded with the core purpose. It contains many details in a prose format; while it is information-dense, it could be slightly more structured (e.g., bullet points for scenarios). However, it wastes no words and every 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 7 parameters, no output schema, and complex behavioral nuances (semantic vs lexical, truncated results, exact mode), the description covers all necessary aspects. It even includes meta-instructions on presenting information as direct knowledge. Error handling (truncated, empty results) is addressed. The description is completely sufficient for an agent to use the tool correctly.

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 explains each parameter. The description adds significant value by explaining behavioral implications (e.g., effect of exact, digest, domain) and usage hints (e.g., 'For unique identifiers known to appear verbatim, also set exact:true'). While the schema is thorough, the description enhances understanding beyond the schema.

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 'Search memories by text across label, description, why_matters, and tags.' It uses a specific verb and resource, and distinguishes itself from sibling tools like 'recall' (which retrieves full content by ID) and 'audit' (which handles archived/stale modes). No ambiguity.

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?

Provides explicit guidance on when to omit domain (entire workspace), when to scope to a domain, when to use exact:true (for identifiers), and when to use audit if searching for non-live entries. Includes a step-by-step process for when search misses (try without domain, scope, recall, orient). This clearly tells the agent when to use this tool vs alternatives.

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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