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recall

Recall memories relevant to a query.

Instructions

Recall memories relevant to a query.

Args: query: What to search for (semantic similarity). n_results: Maximum number of results to return. project: Filter to a specific cortical region (project). language: Filter to a specific language. scope: Filter to a specific engram cluster (domain scope). memory_type: Filter by memory system (episodic/semantic/procedural/prospective). reference_date: ISO-8601 date for temporal context (e.g. "2026-04-12"). Defaults to now. agent_id: Namespace identifier — only returns memories stored with the same agent_id. Omit or leave empty to query the global shared namespace.

Returns: JSON array of matching memories with scores.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
queryYes
scopeNo
projectNo
agent_idNo
languageNo
n_resultsNo
memory_typeNo
reference_dateNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior3/5

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

No annotations are provided, so the description must disclose behavioral traits. The description suggests a read-only operation by stating 'recall' and 'returns ... memories,' but it does not explicitly confirm no side effects, required permissions, or rate limits. The behavior is implied but not fully transparent.

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 well-structured with an Args/Returns format and is front-loaded with the purpose. While somewhat lengthy, the parameter descriptions are necessary given the schema coverage. Every sentence contributes, but minor redundancy could be trimmed (e.g., default values for n_results are both in schema and description).

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?

Given the complexity (8 parameters, output schema exists) the description is fairly complete. It covers all parameters, return format, and basic semantics. It lacks mention of edge cases (e.g., empty results, error handling) and sorting order, but the output schema presumably defines the exact structure. Overall adequate for a retrieval tool.

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?

With 0% schema description coverage, the description provides essential explanations for each of the 8 parameters, including defaults, examples (e.g., reference_date ISO-8601), and semantics (e.g., agent_id for namespace). This fully compensates for the schema's lack of descriptions and adds significant value.

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 'Recall memories relevant to a query.' The verb 'recall' and resource 'memories' precisely describe the operation. It distinguishes itself from sibling tools like store (write), forget (delete), and stats (aggregate), making the tool's purpose unambiguous.

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

Usage Guidelines3/5

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

The description implies the tool is for querying memories but does not explicitly state when to use it vs. alternatives (e.g., stats for aggregated data, or ingest for adding data). No exclusions or prerequisites are mentioned, leaving the agent to infer usage context from sibling names.

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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