kage_learn

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

The description discloses important behavioral details beyond the readOnlyHint=false annotation: the write commits under .agent_memory/, rejects writes when cited paths are missing, scans for secrets/PII before writing, and returns the new packet id plus contradiction warnings. This gives an agent a clear picture of side effects, validation, and output behavior.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is dense but efficient: it front-loads the core action, then gives usage timing, a preference rule, behavioral caveats, and the return value. Every sentence earns its place with no filler or repetition of schema content.

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

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

For a 13-parameter tool with no output schema, the description provides a complete mental model: what the tool does, when to use it, its side effects, its validation rules, and what it returns. The rich schema descriptions fill in the remaining parameter-level details, so an agent has enough to select and invoke the tool correctly.

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

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

Schema description coverage is 100%, so the baseline is 3, but the description adds meaningful operational meaning for paths and allow_missing_paths by explaining the rejection condition and when to set the flag. It does not add semantics for every parameter, but the schema already covers the remaining ones.

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

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

The description clearly states a specific action and resource: capturing a durable, reusable learning as a repo-local memory packet committed under .agent_memory/. It is precise about the object and purpose, though it does not explicitly differentiate among the sibling tools like kage_supersede or kage_skills; it only contrasts with diff-based proposals.

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

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

It gives explicit use-case guidance: use it the moment you discover something a future session should know, with concrete examples such as decisions, bug root causes, conventions, and setup steps. It also says to prefer it over diff-based proposals when you already know what was learned, but it does not describe when-not-to-use it relative to named sibling tools.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.