List Favorite Courses

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

Annotations already declare readOnlyHint, idempotentHint, and destructiveHint=false, so safety is covered; the description adds real value by explaining that the favorites drive dashboard contents. It does not describe the return format or ordering, which keeps it below a 5.

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?

Three short clauses, each doing distinct work: what it returns, why it matters, and where to mutate. Purpose is front-loaded with zero filler.

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 read tool with rich annotations and no output schema, the description covers purpose, effect, and mutation alternatives. A brief note on what each favorite record contains would make it fully self-sufficient.

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 zero parameters, so the schema cannot carry any semantics and there is nothing for the description to clarify. The description correctly implies no filtering or arguments are required, but adds no parameter-level detail beyond that.

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 ('List the courses you have starred as favorites') and immediately adds the effect on the dashboard, which disambiguates it from generic listing tools like list_courses. Sibling routing is made explicit by naming add_favorite_course and remove_favorite_course.

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?

Tells the agent exactly when to use this tool (to read the current favorites) and names the alternatives for changing them, with the condition that selects each. Nothing about the read-vs-mutate choice is left to inference.

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