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Recall XMemo memory

recall
Read-onlyIdempotent

Fetch relevance-ranked memories based on a natural-language query, with filters for type, path, and optional working memory preference. Use for quick lookups before answering.

Instructions

Recall a few relevance-ranked memories before answering, with path/type controls and optional working-memory preference. Use it for a quick best-effort lookup; use recall_context when the answer needs a bounded multi-memory context pack.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNoMaximum number of results to return.
queryYesNatural-language question or search text.
explainNoWhether to include retrieval explanation details with search or recall results.
agent_idNoOptional client-supplied agent label for memory attribution.
memory_typeNoMemory type/category filter or value, such as episodic, identity, procedural, semantic, working, auto, or %.%
path_filterNoMemory path filter; % matches all paths.%
prefer_workingNoWhether to prioritize working/session-state memories in retrieval.
agent_instance_idNoOptional stable, non-secret agent instance ID for per-client attribution.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior4/5

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

Annotations already declare readOnlyHint=true and destructiveHint=false, covering the safety profile. The description adds behavioral context beyond these annotations: results are 'relevance-ranked,' and the tool supports 'path/type controls and optional working-memory preference.' It also implies a best-effort retrieval strategy. This is meaningful, though it doesn't detail output structure or edge cases.

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?

The description is exactly two sentences and is highly efficient. The first sentence conveys the core action and key features; the second provides usage differentiation. No filler or redundancy, and the most important information is front-loaded.

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 read-only retrieval tool with a well-covered schema and declared output schema, the description is sufficient. It explains what the tool does, when to use it, and how it differs from the closest sibling. The presence of an output schema means return values need not be described. Minor gaps such as not explaining the 'explain' parameter are covered by the schema.

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?

The input schema provides descriptions for all 8 parameters (100% coverage), so the baseline is 3. The description's mention of 'path/type controls' and 'working-memory preference' maps to path_filter, memory_type, and prefer_working, but adds no new semantic detail beyond the schema. Each parameter already has a clear description, so the description adds no extra value here.

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 tool's function: 'Recall a few relevance-ranked memories before answering,' specifying the resource (memories) and the action (recall). It also distinguishes itself from the sibling tool recall_context by contrasting quick best-effort lookup with a bounded multi-memory context pack. This goes beyond a simple restatement of the title.

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?

Explicit usage guidance is provided: 'Use it for a quick best-effort lookup; use recall_context when the answer needs a bounded multi-memory context pack.' This directly tells the agent when to prefer this tool over the most relevant alternative, satisfying the dimension fully.

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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