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sage_remember

Store durable facts or ephemeral observations to retain context across conversations. Use memory types to keep verified knowledge and session details.

Instructions

Store a memory in SAGE. Use this to save facts, observations, or inferences that should persist across conversations. IMPORTANT: Use type='fact' (confidence 0.95) for durable knowledge that should persist long-term and be visible across all agents — infrastructure details (IPs, hostnames, SSH commands, URLs, ports), architecture decisions, verified configurations, credentials paths, and server specs. Use type='observation' for ephemeral session context. Facts survive confidence decay and cross provider boundaries; observations do not.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
confidenceNoConfidence score 0-1
contentYesThe memory content to store
domainNoDomain tag (e.g. general, security, code)general
tagsNoUser-defined labels for this memory (e.g. 'important', 'project-x')
typeNoMemory type. fact (0.95+): verified durable knowledge — IPs, hostnames, architecture decisions, configs, infrastructure. observation (0.80): session-level context — what happened, what was discussed. inference (0.60): hypotheses and conclusions. task: actionable items.observation
Behavior4/5

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

With no annotations, the description carries full burden. It explains that type affects persistence and cross-boundary visibility, and mentions confidence decay. Minor omission of error handling or idempotency, but sufficient for a store operation.

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?

Six sentences, front-loaded with purpose, clear structure. Each sentence adds necessary detail without redundancy. The important note is highlighted with 'IMPORTANT:'.

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 5 parameters (only content required), no output schema, and full schema coverage, the description is comprehensive. It explains memory types well. Lacking detail on return value or error handling, but not critical for tool 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?

Input schema has 100% coverage with descriptions. The description adds value by expanding on the 'type' enum with concrete examples and usage guidance, but the schema already provides solid foundations.

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 stores a memory in SAGE and differentiates between fact and observation types. It specifies the scope and persistence, distinguishing it from siblings like sage_recall and sage_forget.

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?

The description explicitly instructs when to use type='fact' vs type='observation', including confidence thresholds and examples of durable vs ephemeral content. It implicitly guides against misuse.

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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