get_learnset

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. However, it fails to describe any behavioral traits such as whether this is a read-only operation, what data is returned, potential rate limits, or error conditions. It provides no insight beyond the minimal name restatement.

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 extremely concise with a single phrase, but this brevity comes at the cost of under-specification rather than efficient communication. It's front-loaded but lacks substance, making it more of a placeholder than a helpful description.

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?

Given the complexity of a 3-parameter tool with no annotations, no output schema, and 0% schema coverage, the description is completely inadequate. It doesn't explain what a 'learnset' is, how the parameters interact, what the output looks like, or any operational context, leaving critical gaps for the agent.

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?

With 0% schema description coverage and 3 parameters (name, version_group, method), the description adds no meaning beyond what the input schema provides. It mentions 'version_group' and 'método' (method) but doesn't explain their purposes, valid values, or how they affect the learnset retrieval, failing to compensate for the lack of schema documentation.

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 'Learnset por version_group y método' is a tautology that essentially restates the tool name 'get_learnset' in Spanish, providing no additional clarity about what the tool actually does. It doesn't specify what resource is being retrieved (Pokémon learnset data) or what the verb 'get' entails, making it vague and minimally informative.

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?

There is no guidance on when to use this tool versus alternatives like 'get_pokemon', 'suggest_moveset', or 'validate_moveset' from the sibling list. The description offers no context about appropriate use cases, prerequisites, or exclusions, leaving the agent with no usage direction.

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