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LogicLabsAI

UltraMemory

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by LogicLabsAI

Playbook Recall

playbook_recall
Read-only

Search past successful strategies for any situation using natural language queries. Get credit-scored results to reuse proven approaches.

Instructions

Retrieve strategies that have worked before for this situation (learned, credit-scored).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
kNoMax results (default 10)
queryYesNatural-language question or topic to search memory for
scopeNoProject scope id (default 'default')default

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
countNoNumber of strategies returned
resultsNoMatching playbook strategies (entry_id, trigger, strategy, credit, uses, wins, score)
Behavior4/5

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

Annotations already declare readOnlyHint=true; description adds 'learned, credit-scored' and 'worked before,' reinforcing read-only nature and scoring behavior without 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?

Single sentence, front-loaded verb and resource, no wasted words.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given output schema exists and tool is simple, description covers core purpose but lacks usage guidance to differentiate from siblings, leaving gaps in contextual completeness.

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% and parameter descriptions are clear; description does not add meaning beyond what the schema already 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?

Description uses specific verb 'retrieve' and resource 'strategies' (playbook), adds context 'learned, credit-scored' to differentiate from generic memory tools.

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

Usage Guidelines2/5

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

No guidance on when to use this tool versus alternatives like memory_recall, search, or fetch. With 8 sibling tools, explicit context is missing.

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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