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store

Store a memory with tags, context, importance, and namespace. Build a persistent, correctable AI memory that surfaces corrections first.

Instructions

Store a memory.

Args:
    content: The memory content to store.
    tags: Comma-separated tags (e.g. "preference,ui,dark-mode").
    context: When/where this memory applies.
    importance: 1-10, how important this memory is.
    namespace: Namespace for organizing memories.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
tagsNo
contentYes
contextNo
namespaceNodefault
importanceNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior2/5

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

No annotations are provided, so the description must convey behavioral traits. It does not disclose whether storing can overwrite existing memories, require specific permissions, or have side effects. The tool modifies state, but no idempotency or limiting behavior is mentioned.

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 well-structured with a clear purpose statement followed by an Args list. It is concise for a 5-parameter tool, though the list format adds verbosity. Every sentence serves a purpose in defining the parameters.

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 output schema exists, the description does not need to cover return values. It explains all input parameters adequately for a simple store operation. However, it could provide more context about how the memory is stored (e.g., persistence) or constraints like namespace uniqueness.

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 description coverage is 0%, and the description compensates by explaining each parameter's purpose and format, e.g., 'Comma-separated tags' and '1-10, how important.' This adds significant value beyond the schema's defaults and titles.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description states 'Store a memory,' clearly indicating the verb and resource. While it distinguishes the tool from siblings like 'forget' and 'recall' by implying creation vs deletion/retrieval, it does not explicitly differentiate from similar tools like 'correct'.

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?

Usage guidelines are implied: use when you need to save a memory. However, there is no explicit guidance on when to use this tool vs alternatives (e.g., 'correct' for updating) or when not to use it. The description lacks exclusions or context for choosing among siblings.

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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