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ArkaAiAdmin

Agentic Memory

by ArkaAiAdmin

memory_curate_autosave

List auto-saved notes from tool invocations and batch-promote or discard them to create intentional lessons or decisions.

Instructions

Review auto-saved tool invocations and promote or discard them.

The agent can list auto-saved notes, then batch-promote them into intentional lessons or decisions with epistemic_source='agent'.

Args: start_date: ISO date filter start (e.g. "2026-06-01"). Empty = no start bound. end_date: ISO date filter end (e.g. "2026-07-01"). Empty = no end bound. action: "list" | "promote" | "discard". note_ids: List of note IDs to promote/discard (required for promote/discard). category: Target category for promotion (default "lessons").

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
actionNolist
categoryNolessons
end_dateNo
note_idsNo
start_dateNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior3/5

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

With no annotations provided, the description adds some behavioral context: it notes that promotion sets `epistemic_source='agent'`, which is a specific side effect. It also clarifies that `note_ids` are required for promote/discard actions. However, it does not disclose whether discarding deletes auto-saves permanently or other side effects, leaving some behavioral gaps.

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 concise, with two paragraphs. The first sentence captures the core purpose, and the 'Args:' block lists parameters efficiently. While it is not overly verbose, the structure could be slightly improved by front-loading the action parameter more prominently.

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 the tool's complexity (curating auto-saves) and the presence of an output schema, the description does not need to detail return values. It explains the primary operations and their effects. However, it could be more complete by clarifying the state changes (e.g., whether promoted notes replace auto-saves or are created separately).

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?

The input schema has 0% description coverage, but the tool's 'Args:' block provides clear, concise explanations for all 5 parameters: start_date, end_date, action (with valid values), note_ids (required for promote/discard), and category (default 'lessons'). This adds significant meaning beyond the schema's titles and defaults.

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 reviews auto-saved tool invocations and allows promoting or discarding them. It specifies the actions (list, promote, discard) and the resource (auto-saved notes). This distinguishes it from sibling memory tools like memory_save or memory_recall, which serve different purposes.

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?

The description explains that the agent can list auto-saved notes, then batch-promote or discard them. It implies a sequence of use and sets context for when to use this tool (for curating auto-saves). However, it does not explicitly state when not to use it or mention alternatives, though the context is clear enough for an agent.

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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