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capture

Manually save knowledge items like facts, design notes, or troubleshooting steps to your local memory with rationale and tags for future recall.

Instructions

Save a knowledge item to your LOCAL scope: a fact, design note, gotcha, or troubleshooting step worth remembering. This is the manual capture path; distill() is the automatic one.

type : memory | need | skill kind : freeform subtype (note, decision, constraint, runbook, ...) why : the rationale — makes the item far more useful at recall time tags : comma/space-separated keywords (boost recall matching) valid_while : optional premise this knowledge depends on, e.g. "while we're on NetSuite" — surfaced later so a dead assumption is spottable

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
whyNo
kindNonote
tagsNo
typeNomemory
contentYes
valid_whileNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior2/5

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

With no annotations provided, the description carries the full burden but fails to disclose behavioral traits such as mutability, side effects, authentication needs, or persistence behavior. Mentioning 'LOCAL scope' hints at scope but does not clarify safety or limitations.

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 bullet-like lines for parameters after an initial clear statement. It is slightly verbose but every sentence adds value, making it efficient and easy to parse.

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

Completeness3/5

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

The tool has 6 parameters and an output schema (not shown), yet the description omits the return format or response behavior. It explains input well but lacks completeness about what the caller receives after execution.

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 description coverage is 0%, but the description compensates fully by explaining each parameter's meaning: 'type' as memory|need|skill, 'kind' as note/decision/etc., 'why' as rationale, 'tags' as keywords, and 'valid_while' as dependency premise. This adds significant value beyond the raw 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 tool saves a knowledge item to 'LOCAL scope' using a manual capture path, distinguishing it from the automatic sibling 'distill()'. The verb 'save' and resource 'knowledge item' are specific, and the scope is explicitly defined.

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 provides explicit guidance on when to use this tool ('manual capture path') vs. the alternative 'distill()' (automatic). It does not address other siblings like 'record_need' or 'recall', so it is not exhaustive but is clear enough for the primary distinction.

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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