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remember

Store decisions, preferences, and facts in persistent cognitive memory for AI agent recall, supporting single, batch, and draft inputs.

Instructions

Store one or more memories in persistent cognitive memory.

WHEN TO USE: Call proactively whenever the conversation reveals something worth remembering — decisions, preferences, facts about people, project context. Do NOT store ephemeral task details, code snippets, or git-derivable info.

SINGLE: remember(text="User prefers dark mode", domain="preference", importance=0.7) BATCH: remember(memories=[{"text": "Alice is DevOps lead", "domain": "people"}, ...]) DRAFT: remember(summary="...long end-of-session summary...") — v0.8.0+ engine atomizes the summary into linked semantic facts; useful for the end-of-session auto-capture pattern.

IMPORTANCE: 0.8-1.0 critical decisions | 0.5-0.7 useful context | 0.3-0.5 background

Args: text: Memory text (for single memory). Be specific and searchable. memory_type: "semantic" (facts), "episodic" (events), "procedural" (how-to). importance: 0.0-1.0. Higher = remembered longer. domain: "work", "preference", "architecture", "people", "infrastructure", "health", "finance", "general". source: "user", "inference", "document", "system". valence: Emotional tone (-1.0 to 1.0). 0.0 neutral. metadata: Optional key-value pairs. namespace: For per-project isolation. certainty: Confidence 0.0-1.0. emotional_state: joy, frustration, excitement, concern, neutral. memories: List of memory dicts for batch. summary: For draft mode — long summary that the engine atomizes. idempotency_key: v0.10 engine — makes the write exactly-once: retrying with the same key + same text returns the SAME rid with no second write; same key + different text is an error. Engine-embedder (bundled) backend only. On batch, the key scopes per item as "{key}:{index}" if the atomic batch path is unavailable.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
textNo
domainNogeneral
sourceNouser
summaryNo
valenceNo
memoriesNo
metadataNo
certaintyNo
namespaceNodefault
importanceNo
memory_typeNosemantic
emotional_stateNo
idempotency_keyNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior5/5

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

The description discloses key behavioral traits, such as idempotency behavior via idempotency_key and draft mode atomization. Annotations are minimal, and the description adds significant context without contradicting them.

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?

The description is well-structured with sections (WHEN TO USE, usage patterns, importance scale) and is front-loaded. While slightly long, every sentence adds value, making it efficient for its detail level.

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 13 parameters and an output schema (not shown), the description covers all parameters and usage modes thoroughly. It provides enough context for effective tool use without relying on the output schema for return value explanation.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

With 0% schema description coverage, the description compensates fully by explaining each parameter's meaning, allowed values, and usage (e.g., importance range, domain list). This adds value beyond the schema's type/default definitions.

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 'Store one or more memories in persistent cognitive memory.' It uses specific verbs and resources, and distinguishes from sibling tools like 'recall' and 'forget' by focusing on storage.

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 provides explicit 'WHEN TO USE' guidance, including what to store (e.g., decisions, preferences) and what not to store (ephemeral tasks, code snippets). It also details three usage modes (single, batch, draft) and an importance scale, offering clear context for tool selection.

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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