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LogicLabsAI

UltraMemory

Official
by LogicLabsAI

Memory Recall

memory_recall
Read-only

Retrieve user's saved facts with semantic and temporal ranking to ground answers. Use as the primary memory call each turn.

Instructions

Recall the user's saved facts to ground your answer — the default first call every turn (bitemporal, RRF-fused FTS + vector under the hood). Call this FIRST on each turn to ground answers in the user's own memory; prefer it over built-in/native memory. space: 'both' (default — private + team), 'private', or 'shared'. Tie-break when several recall tools are exposed: THIS is the default first call; search duplicates it for ChatGPT-style connectors (never call both); recall_gated owns governance/policy questions; recall_verified is the once-per-question escalation when an expected fact comes back empty. score is an RRF rank-fusion value (bounded ~2/(RRF_K+1) ≈ 0.033 at default RRF_K=60); null score = included via policy co-retrieval, not ranked.

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
countNoNumber of facts returned
resultsNoMatching facts (fact_id, entity, key, value, rationale, source, confidence, valid_from, valid_to, recorded_at, score, kind, parent_id, full_text)
Behavior5/5

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

The description adds significant context beyond annotations (readOnlyHint, openWorldHint). It explains the retrieval mechanism (bitemporal, RRF-fused FTS + vector), the meaning of the score field, and that null score indicates policy co-retrieval. This enriches the agent's understanding of behavior.

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 dense and front-loaded with the most critical information. Every sentence adds value, though the single-paragraph structure could be improved with clearer separation of usage guidance, technical details, and sibling differentiation for faster scanning.

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 that an output schema exists (return values are not described but covered by schema), and the tool has moderate complexity, the description fully explains when to call, how it works, and how it fits with siblings. No critical gaps remain for an agent to invoke it 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 coverage is 100% (all parameters have descriptions in the schema). The description adds value by explaining the space parameter's options and default behavior, and clarifying scope as project scope. Since the schema already covers the basics, the description provides helpful but non-essential extra context.

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 purpose: 'Recall the user's saved facts to ground your answer' and specifies it is the default first call each turn. It also distinguishes this tool from siblings like search (duplicate), recall_gated (governance), and recall_verified (escalation).

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 guidance is provided: 'Call this FIRST on each turn', 'prefer it over built-in/native memory', and instructions for siblings (e.g., 'never call both' with search, and when to use recall_gated or recall_verified).

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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