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memory_episode_save

Store an event episode with emotional weight and tags into persistent episodic memory for AI agents.

Instructions

Save an episode to L3 episodic memory.

Args: layer: "user" or "agent" user_id: User identifier summary: Episode description weight: Emotional weight 0.0-1.0 (default 0.5) tags: Tags (e.g. ["greeting", "decision", "error"])

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
tagsNo
layerNouser
weightNo
summaryNo
user_idNodefault
Behavior2/5

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

With no annotations, the description carries full burden for behavioral disclosure. It only states the action (save), with no mention of side effects, persistence, or reversibility. Important behavioral traits like whether saving an episode overwrites existing data are missing.

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, with a clear one-sentence purpose followed by a structured parameter list. Every line adds value, and there is no redundant or extraneous information.

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

Completeness2/5

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

The description lacks information about return values, side effects, or success/failure indicators. Given the absence of an output schema and annotations, the agent needs more context to use the tool effectively (e.g., whether an ID is returned).

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 description includes an 'Args:' section that provides meaningful explanations for each parameter beyond the schema's types and defaults. For example, it specifies valid values for 'layer' ('user' or 'agent'), 'weight' range (0.0-1.0), and examples for 'tags'.

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 'Save an episode to L3 episodic memory,' specifying the verb 'save' and the resource 'episode.' It distinguishes from sibling tools like memory_episode_get, memory_episode_list, and memory_episode_recall, which are read-oriented.

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

Usage Guidelines2/5

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

The description lacks guidance on when to use this tool versus alternatives (e.g., memory_recall, memory_remember). No context is provided about prerequisites or situational usage, leaving the agent without direction.

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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