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store_memory

Save and persist agent memories across sessions. Stores text content with type and importance ratings for later retrieval.

Instructions

Store a memory for an agent/user pair. Content is embedded locally (ONNX)
and persisted to Postgres. Returns the memory ID on success.

Args:
    agent_id:     Unique identifier for the agent storing the memory.
    user_id:      Unique identifier for the user this memory belongs to.
    content:      The text to remember (max 8000 chars).
    memory_type:  'episodic' (events/conversations), 'semantic' (facts/preferences),
                  or 'procedural' (workflows). Default: episodic.
    importance:   Weight multiplier 0.0–2.0. Use >1.0 for critical facts. Default: 1.0.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
agent_idYes
user_idYes
contentYes
memory_typeNoepisodic
importanceNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior4/5

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

No annotations are provided, so the description carries the full burden. It discloses key behaviors: local embedding (ONNX), persistence to Postgres, return of memory ID, content limits (8000 chars), and memory type options. Missing details on auth needs or rate limits, but adequate for the domain.

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 compact and well-structured with a brief intro followed by a parameter list. Every sentence adds value, no redundancy.

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 complexity (5 params, no annotations) and the presence of an output schema, the description provides sufficient context: purpose, parameter details, and return value. Missing some behavioral nuances like error handling or idempotency, but overall complete enough for an agent.

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 description thoroughly explains each parameter: agent_id and user_id as identifiers, content with max chars, memory_type with enumerated values and default, importance with range and usage guidance. This adds significant meaning beyond the schema.

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 verb 'Store' and the resource 'memory for an agent/user pair', and distinguishes it from siblings (count, delete, search) through its focus on creation.

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

Usage Guidelines3/5

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

The description implicitly indicates use for storing memories but lacks explicit guidance on when to use this versus alternatives or when not to use it. No exclusions or alternatives are mentioned.

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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