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sage_turn

Recall relevant consensus-committed memories for context and store a concise observation of the current turn to build episodic experience across sessions.

Instructions

Per-conversation-turn memory cycle. Call this EVERY turn. It does two things atomically: (1) Recalls consensus-committed memories relevant to the current topic (so you have context), and (2) Stores an observation about what just happened in this turn (so future-you has context). This builds episodic experience turn-by-turn, like human memory — not a context window dump. Domains are dynamic: create whatever domain fits the conversation (e.g. 'quantum-physics', 'go-debugging', 'user-project-x'). You decide what's relevant to recall based on the conversation context.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
domainNoKnowledge domain — create dynamically based on the topic (e.g. 'rust-async', 'user-preferences', 'sage-architecture'). Don't reuse 'general' when a specific domain fits better.
observationNoWhat happened this turn — the user's request and key points of your response. Keep it concise but capture the essential insight.
topicYesWhat the current conversation is about — used for contextual recall
Behavior4/5

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

Despite no annotations, the description details the atomic two-step operation (recall and store) and dynamic domain creation. It could further clarify idempotency or side effects, but the core behavior is transparent.

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?

Concise yet comprehensive; front-loaded with the imperative instruction. Every sentence adds meaningful context without redundancy. Structured logically from purpose to specifics.

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 the complexity of a memory cycle with dynamic domains and no output schema, the description provides all essential information for correct tool invocation: the turn-by-turn loop, atomic actions, and dynamic domain selection.

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 descriptions already cover parameters (100% coverage). The description adds value by explaining the dynamic domain usage and the overall memory cycle context, reinforcing and extending the schema hints.

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 as a per-turn memory cycle performing recall and storage. It uses specific verbs ('recalls', 'stores') and distinguishes itself from other memory tools by being the default per-turn handler.

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 instructs 'Call this EVERY turn', providing clear when-to-use guidance. Implicitly differentiates from siblings like sage_remember or sage_recall by being the standard per-turn memory operation.

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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