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Remember

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Idempotent

Store user preferences, coding standards, and project details persistently to recall them in future conversations. Automatically detects memory scope (user or workspace) and language specificity from context.

Instructions

Store user information persistently for future conversations. When users share preferences, coding standards, project details, or any context they want remembered, use this tool. Extract the key information from natural language and store it appropriately. The system automatically detects scope (user/workspace) and language specificity from context. For ambiguous cases, you will receive clarification prompts to ask the user. Examples of what to remember: coding preferences ('I like detailed docstrings'), project specifics ('This app uses PostgreSQL'), language standards ('For Python, use type hints'), workflow preferences ('Always run tests before committing'). Use only the memory_item parameter with natural language - the system handles scope detection.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
memory_itemYesThe information to remember
scopeNoMemory scope: 'user' (default) or 'workspace'user
languageNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior3/5

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

Adds details about automatic scope/language detection and natural language input, but contradicts the schema by advising to 'use only memory_item' while schema exposes scope and language parameters.

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?

Well-structured with examples, but slightly verbose; could tighten the guidance on parameter usage.

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?

Covers use cases, examples, and auto-detection behavior, but could clarify the role of the optional parameters and expected return values.

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

Parameters2/5

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

Provides context for memory_item but downplays scope and language parameters despite their existence in the schema, creating confusion for the agent.

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 persistent context, gives concrete examples, and differentiates from sibling tools like configure_memory_optimization.

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 describes when to use (e.g., preferences, coding standards) and mentions clarification prompts for ambiguity, but lacks explicit when-not-to-use guidance.

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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