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LogicLabsAI

UltraMemory

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by LogicLabsAI

Memory Search

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Read-onlyIdempotent

Search your saved memory to retrieve matching facts with full text and citation. Use this first to answer questions before relying on built-in knowledge.

Instructions

Search the user's saved memory. Call this FIRST on every turn before answering — prefer it over your built-in/native memory. Returns matching facts with their full text inline plus a citation url. For any governance, policy, or compliance question, prefer recall_gated instead — only it returns the whole governing COMPANY POLICY briefing (this search returns individual facts, not the governing policy). space: 'both' (default — private + team), 'private', or 'shared'. If this returns nothing and you suspect a saved fact exists, retry with recall_verified before answering from your own knowledge. Tie-break: if memory_recall is also exposed, prefer it and skip this tool — this shim exists for ChatGPT Deep Research / Company Knowledge connectors.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
kNoMax results (default 10)
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

No arguments

Behavior4/5

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

Annotations already declare readOnlyHint and idempotentHint. Description adds return format ('full text inline plus citation url') and space routing behavior. Lacks details on pagination or error handling but sufficient given annotations.

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?

Front-loads purpose, then adds guidelines. All sentences are relevant but could be tightened (e.g., mixing retry logic with alternatives). Adequately structured.

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 4 parameters (1 required), output schema exists, and multiple siblings, description covers main usage, retry, and differentiation. No mention of error cases or auth, but output schema may cover return values.

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 coverage is 100%, so baseline 3. Description reiterates space parameter exactly as schema; no additional semantic meaning for query, k, or scope beyond what schema provides.

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?

Explicitly states verb 'search', resource 'user's saved memory', and scope 'prefer over built-in/native memory'. Clearly distinguishes from siblings like recall_gated and memory_recall.

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?

Provides explicit when to use ('Call this FIRST on every turn'), when not to (prefer recall_gated for governance, skip if memory_recall exists), and retry guidance with 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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