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LogicLabsAI

UltraMemory

Official
by LogicLabsAI

Verified Recall (high precision)

recall_verified
Read-only

Searches memory with cross-encoder reranking to verify relevance before answering, reducing false abstains on answerable questions.

Instructions

Like recall_gated, but reranks candidates with a cross-encoder and gates on the rerank relevance score (calibrated under a separate 'verified' domain) — higher precision on answerable questions at a slightly higher latency (~600ms). Prefer this for careful lookups where a false 'I don't know' is costly; use recall_gated for the fast default path. Returns the same answer | verify | abstain + sectioned briefing shape.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
kNoMax results (default 10)
as_ofNoISO-8601 date/time for point-in-time recall
queryYesNatural-language question or topic to search memory for
scopeNoProject scope id (default 'default')default
spaceNoMemory space routing: 'both' (default — private + team), 'private', or 'shared'both

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultsNoMatching facts (fact_id, entity, key, value, rationale, source, confidence, valid_from, valid_to, recorded_at, score, kind, parent_id, full_text)
decisionNo'answer' | 'verify' | 'abstain' — the metamemory gate's verdict
event_idNoPass to the memory_feedback tool once the user confirms or corrects the answer
confidenceNoThe gate's confidence in the recall
context_blockNoReady-to-use sectioned briefing: facts with validity ranges + provenance labels, usage instructions, and any whole-policy card bodies
policy_appliedNoTrue when a governing team policy card superseded/bound this recall
Behavior5/5

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

Discloses reranking, gating, calibrated domain, latency (~600ms), and return shape beyond annotations' readOnlyHint, providing rich behavioral context.

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?

Two concise sentences with front-loaded core functionality and usage guidance, no superfluous content.

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 5 parameters and existing output schema, description adequately covers use cases, trade-offs, and behavioral differences from siblings.

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?

Schema covers all parameters thoroughly (100% coverage); description does not add new parameter-level meaning beyond noting return shape.

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?

Description clearly states the tool's function: reranking with cross-encoder and gating for high precision, distinguishing it from sibling recall_gated.

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?

Explicitly recommends using this tool for careful lookups where false negatives are costly, and directs to recall_gated for default fast path, with latency trade-off noted.

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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