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

Annotations already establish readOnly, openWorld, and non-destructive behavior. The description adds useful context beyond them: the result contains only active learning paths and returns id and name fields. It does not disclose pagination or result limits, but the core behavioral shape is clear.

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?

Two short sentences with the key scope ('active') and return fields front-loaded. The endpoint path is technical but does not obscure the primary 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?

For a zero-parameter list tool with annotations and no output schema, the description covers the essential scope and return fields. It could mention pagination or whether all active paths are always returned, but nothing critical is missing for correct invocation.

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?

The tool takes no parameters, so the schema cannot provide parameter semantics and the baseline is 4. The description correctly implies an unfiltered list of active learning paths, with no parameter details to compensate for.

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 gives a specific verb (List), resource (active learning paths), and return fields (id and name), plus the endpoint path. It is clearly distinct from sibling tools like learningpath_report and list_learningpath_users, but it does not explicitly name those alternatives.

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 when-to-use guidance, no exclusions, and no explicit comparison to alternatives such as learningpath_report or list_learningpath_users. The 'active' qualifier is the only usage constraint, and the agent must infer everything else.

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