list_enrolled_courses

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

No annotations are provided, so the description must disclose behavioral traits. It clearly states that specializations are flattened into constituent courses, and enumerates the fields in each item (slug, name, type, progress_percent, parent info). This gives the agent a good sense of the output structure and the flattening behavior. However, it does not mention whether there are any limits (e.g., number of courses) or whether the list is sorted or filtered; these minor gaps prevent a perfect score.

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 compact and well-structured: the opening sentence states the purpose, the second sentence explains a key behavior (flattening), the third lists the output fields, and the final sentence gives a usage tip. Every sentence adds value, and there is no redundancy or fluff. It is front-loaded with the core purpose, making it easy to scan.

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 no-parameter list tool with an output schema, the description covers all essential aspects: what it does, how the data is shaped, and how to use the results with other tools. The output schema likely formalizes the structure, so the description adds narrative context without repeating schema details. No critical information 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?

The tool has zero parameters, so the schema is trivially complete (coverage 100%). The baseline for zero parameters is 4, and the description correctly adds no parameter-specific details because there are none. It does mention the output fields, which aids in interpreting what the tool returns but does not affect parameter semantics. A score of 4 reflects the baseline appropriateness.

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 states a precise verb and resource: 'List every course you're enrolled in on learn.deeplearning.ai.' It clearly defines the scope (courses, not lessons) and even details the flattening of specializations, making it unambiguous. While it doesn't name sibling tools explicitly, the distinct purpose is evident and no confusion arises among the listed siblings (list_lessons, get_lesson_transcript, etc.).

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?

The description gives practical usage guidance by instructing the agent to use the returned `slug` for subsequent calls to `list_lessons` or `get_lesson_transcript`. This effectively communicates how the output feeds into other tools. It does not explicitly state when to avoid this tool, but since it is the only course-listing tool among siblings, that is not a critical omission. The guidance is clear and actionable.

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