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Store Memory

neuroverse_store

Store memory records in a tiered system (short-term, episodic, semantic) using importance scoring. Persist only high-importance episodic and semantic memories to JSON.

Instructions

Store a memory record in NeuroVerse's tiered memory system.

Tiers:

  • short_term: In-process, capped at 50 per user. Lost on restart.

  • episodic: Persisted to JSON file. Recent actions.

  • semantic: Persisted to JSON file. Long-term facts.

Only episodic/semantic memories with importance_score ≥ 0.4 are persisted.

Args:

  • user_id (string): Agent / user identifier

  • intent (string): Canonical intent name

  • tier (string): short_term | episodic | semantic

  • language (string): Language code (default: "en")

  • data (object): Structured payload

  • importance_score (number): 0.0–1.0

Returns: JSON of the stored MemoryRecord

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
dataNoStructured memory payload
tierNoMemory tiershort_term
intentYesCanonical intent this memory relates to
user_idYesAgent / user identifier
languageNoPrimary language codeen
importance_scoreNoImportance score — only above 0.4 are persisted for episodic/semantic
Behavior5/5

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

Discloses key behaviors: short-term lost on restart, only episodic/semantic memories with importance_score ≥ 0.4 are persisted. No contradiction with annotations.

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?

Well-structured with bullet points, front-loaded purpose, and no redundant sentences.

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

Completeness5/5

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

Covers behavioral nuances (persistence, loss), return format, and all parameters despite no output schema.

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

Parameters4/5

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

Schema coverage is 100%, but description adds value by explaining importance_score threshold and tier semantics, exceeding baseline.

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 a memory record and explains the three tiers, distinguishing it from siblings like neuroverse_recall.

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?

Provides detailed tier descriptions (short_term for in-process, episodic for recent actions, semantic for long-term facts) and importance threshold, but lacks explicit when-not-to-use or 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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