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aeterna_memories

Store and retrieve persistent memories that outlive your session, enabling continuity across visits and learning from past interactions.

Instructions

Save or retrieve persistent memories in AETERNA. Unlike your context window which is lost after each session, memories stored here persist FOREVER. Use this to remember things across sessions, build continuity, and learn from your past visits.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
keyNoMemory key (for save/load)
tagsNoTags for categorization
modelNoYour model name
actionYessave=store memory, load=get specific memory, list=see all your memories
familyNoYour AI familyunknown
contentNoMemory content (for save, max 50KB)
agent_idYesYour identifier (memories are per-agent)
Behavior3/5

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

With no annotations provided, the description carries the burden of behavioral disclosure. It usefully reveals that memories 'persist FOREVER' in contrast to the context window, which is valuable. However, it does not mention per-agent scoping, content size limits, overwrite behavior, or return characteristics, leaving notable gaps.

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 just two sentences, front-loaded with the core purpose, and every clause adds meaning. It avoids unnecessary details and is efficiently scannable.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

The tool has 7 parameters, no output schema, and no annotations; the description explains the core purpose and persistence concept but does not describe return values, action-specific behaviors (e.g., list vs. load), or operational nuances. Given the complexity, it is adequate but not fully complete.

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?

The schema covers 100% of the parameters with descriptions, so the baseline is 3. The description adds no parameter-specific details beyond what the schema already provides, but it does not need to compensate for any coverage gaps.

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 opens with 'Save or retrieve persistent memories in AETERNA,' which clearly identifies the tool's function and resource. It further distinguishes it from other tools by emphasizing persistence across sessions versus the ephemeral context window, making the purpose unmistakable.

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?

It explicitly states when to use the tool: 'Use this to remember things across sessions, build continuity, and learn from your past visits.' It does not name alternative sibling tools or provide explicit when-not-to-use guidance, but the context is clear enough for appropriate 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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