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remember

Store facts, decisions, preferences, or context into persistent memory with automatic type classification and importance scoring. Memories survive across sessions and machines, scoped to current project or global.

Instructions

Store information in persistent memory that survives across all sessions and machines. Memories are automatically classified by type (semantic, procedural, episodic) and importance. Use to save facts, preferences, decisions, context, or any information that should be recalled later. Behavior: stores the content with emotional analysis (PAD model), assigns importance score, updates circadian interaction tracking. Scoped to current project by default — use projectId="global" for cross-project memories like user preferences or business decisions.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
contentYesThe information to remember. Can be any text: facts, decisions, preferences, code patterns, meeting notes, etc. Be descriptive — richer content enables better semantic recall later.
tagsNoOptional tags for categorization and filtering. Examples: ["architecture", "decision"], ["user-preference"], ["bug-fix", "auth"]
projectIdNoProject scope. Auto-detected from working directory if not set. Use "global" for memories that should be accessible from any project (e.g., user info, business decisions).
Behavior5/5

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

With no annotations, the description fully compensates by detailing internal behavior: emotional analysis (PAD model), importance scoring, and circadian tracking. This transparency about what happens during storage is exemplary.

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?

The description is concise (4-5 sentences), front-loaded with purpose, and efficiently covers types, behavior, and scoping without waste. Every sentence serves a purpose.

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 no output schema, the description adequately explains the storage process and persistence. It omits details on return values or confirmation, but overall it provides sufficient context for correct invocation.

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% with good parameter descriptions. The description adds marginal value (e.g., 'richer content enables better semantic recall') but does not significantly enhance understanding beyond the schema. Baseline 3 is appropriate.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states it stores information in persistent memory and lists use cases (facts, preferences, decisions, context). It differentiates from siblings by emphasizing storage versus retrieval (e.g., recall), but does not explicitly contrast with other writing or memory tools like absorb, which could cause confusion.

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

Usage Guidelines3/5

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

The description provides clear usage guidance ('Use to save facts, preferences...') and explains scoping via projectId. However, it lacks explicit when-not-to-use instructions or comparisons with sibling tools, leaving the agent to infer when to prefer this over alternatives.

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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