learn_word

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

Annotations declare destructiveHint=true and idempotentHint=true, and the description earns credit by explaining exactly what is destructive: re-calling with the same term updates meaning, and aliases/examples are replaced by what you pass, with omission clearing them. It also discloses the return shape ('Returns { data: word }'), which is needed since no output schema exists.

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?

Front-loaded with the identity of the tool, then scope, then update semantics, then return value. Four sentences, no filler, and the parenthetical clarifications are load-bearing rather than decorative.

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 five-parameter mutation tool with no output schema, the description covers what the tool is, how re-invocation behaves, what gets overwritten, how the data is later retrieved, and what comes back. Nothing an agent needs in order to call it correctly is missing.

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 coverage is 100%, so the baseline is 3, but the description adds a genuine nuance beyond the schema: that leaving aliases/examples out clears them, which turns an optional field into a destructive default. It adds nothing extra about namespace scoping, which is left entirely to the schema.

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?

States a specific verb and resource ('Learn a private word') and immediately disambiguates with concrete examples (nickname, pet name, in-joke, project name) that distinguish it from generic memory tools like remember. It also names the siblings it interacts with (recall, wake_up), so the agent can place it in the toolset without opening the schema.

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?

Gives clear operational context: learned words are matched literally in recall and surfaced at wake_up, which tells the agent where this tool fits in the workflow. It does not explicitly contrast against remember or list_memories (e.g. 'use this instead of remember for private vocabulary'), so the routing guidance is strong but not exhaustive.

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