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store_memory

Persist key information from your current work as searchable, tagged memories for retrieval in future sessions.

Instructions

Store knowledge in the Brain for future sessions.

Args: content: The information to remember (markdown supported). title: Short descriptive title for this memory. project: Project name (use 'global' for cross-project knowledge). tags: Keywords for easier retrieval (e.g. ["python", "fastapi", "auth"]). category: One of: architecture, bugfix, config, pattern, context, reference, note.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
tagsNo
titleYes
contentYes
projectNoglobal
categoryNonote

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 carries the full burden of behavioral disclosure. It reveals that content supports markdown, project scoping defaults to global, and category has a fixed set of values. However, it does not address potential overwriting, duplicate handling, or permissions, leaving some behavioral ambiguity for a write operation.

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 and well-structured, with a single opening sentence and a clean Args list. Every line adds value, no redundancy or fluff.

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?

The description covers all parameters and the main purpose, and an output schema exists to define the return value. However, it leaves some contextual gaps (e.g., behavior on duplicate titles, whether memories can be overwritten), which prevents a perfect score. Given the tool's simplicity, it is still largely complete.

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 schema provides no descriptions for parameters, but the description compensates fully by explaining each parameter: content's markdown support, title's purpose, project's 'global' convention, tags with an example, and an explicit enumeration of category values. This exceeds schema information and makes the parameters self-explanatory.

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's function with a specific verb ('Store') and resource ('knowledge in the Brain for future sessions'), distinguishing it from sibling tools like recall, list_memories, update_memory, and delete_memory. The intent is unambiguous.

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 establishes a clear use case (storing new knowledge for future sessions) but does not explicitly mention alternatives or exclusions. Context is clear, but there's no guidance on when to prefer this over store_decision or update_memory, so it earns a 4 rather than 5.

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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