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sage_turn

Every turn, recall consensus-committed memories relevant to the current topic and store an observation to build episodic context across conversations.

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?

No annotations provided, so description carries full burden. It transparently describes the two atomic actions (recall and store) and dynamic domain creation. Lacks details on failure cases or limits, but overall behavior is clear.

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?

Description is well-structured with clear opening, explanation of actions, and domain advice. Every sentence adds value; the human memory analogy is helpful. Could be slightly more concise but remains efficient.

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?

For a 3-parameter tool with no output schema, the description explains the workflow (every turn) and decision-making for recall. Lacks details on edge cases (e.g., no memories found), but overall adequate given the complexity.

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%, baseline 3. Description adds meaningful context: examples and creation guidance for 'domain', what to include in 'observation', and purpose of 'topic'. Goes beyond mere parameter names.

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 'Per-conversation-turn memory cycle' and explains its two atomic actions: recall and store. It distinguishes from sibling tools like sage_recall and sage_remember by being a combined, turn-level operation.

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

Usage Guidelines4/5

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

Explicitly says 'Call this EVERY turn' and provides guidance on dynamic domains. While it doesn't explicitly state when not to use it (e.g., for recall-only tasks), the instructions are clear for its intended use.

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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