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Build XMemo context

recall_context
Read-onlyIdempotent

Build a context pack from saved memories to inform answers and plans. Retrieve preferences, prior conversations, projects, decisions, and TODOs using semantic search.

Instructions

Build a context pack from XMemo memories. Call this before answering or planning when the task could benefit from multiple saved memories — preferences, prior conversations, projects, decisions, TODOs, or long-running work.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNoMaximum number of results to return.
queryYesNatural-language question or search text.
scopeNoOptional scope that narrows memory access; leave blank for the token default.
bucketNoMemory bucket or namespace to read from or write to; use % only for tools that support wildcard reads.%
team_idNoOptional team/workspace identifier for team-scoped memory access.
agent_idNoOptional client-supplied agent label for memory attribution.
max_itemsNoMaximum number of memory items to include.
max_tokensNoApproximate maximum response size.
memory_typeNoMemory type/category filter or value, such as episodic, identity, procedural, semantic, working, auto, or %.auto
output_jsonNoReturn a machine-readable JSON response instead of a human-readable summary.
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
Behavior3/5

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

Annotations already indicate readOnlyHint=true, destructiveHint=false, idempotentHint=true. The description adds no new behavioral details beyond stating it builds a context pack, which is consistent. No contradiction.

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?

Description is two sentences, front-loaded with purpose followed by usage guidance. Every sentence adds value with no redundancy.

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 13 parameters and an output schema (as per context signals), the description is concise but sufficiently covers the tool's main function and when to call it. It does not detail return values, but output schema exists to cover that.

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?

Input schema has 100% description coverage. The description does not add meaning beyond the schema, so baseline score of 3 is appropriate.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool builds a context pack from XMemo memories, with examples of what it retrieves. It distinguishes from siblings by emphasizing multi-memory context gathering, but does not explicitly differentiate from similar tools like recall or search_memory.

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

Usage Guidelines4/5

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

The description provides explicit usage guidance: call before answering or planning when multiple saved memories could benefit. It lists specific use cases (preferences, prior conversations, etc.), but does not explicitly state when not to use it or name alternative tools.

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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