list_users

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. It correctly implies a read-only operation ('List'), but does not disclose any edge cases, such as pagination, permission requirements, or behavior when no users match. It adds no nuance beyond the obvious read nature, so it is adequate but minimal given the absence of annotations.

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 a single, concise sentence that front-loads the action and scope, enumerating the exact filter values with zero redundancy. Every word contributes to understanding, and it is immediately scannable by an agent.

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 presence of an output schema (which covers return structure), the description does not need to explain response details. It adequately covers the tool's purpose and the main parameter semantics. The only minor gap is the lack of explicit mention of prerequisites (e.g., obtaining a valid course_identifier), but this is implied and not critical for a simple list operation.

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 description coverage is 0%, so the description must compensate. It adds meaning to the enrollment_type parameter by listing all valid values (student, teacher, ta, observer, designer) and clarifies that users are scoped to a course, which partially covers the course_identifier parameter. However, it does not explain the format or possible identifiers (e.g., SIS ID vs. numeric ID), leaving some semantic gaps.

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 clear verb ('List'), a specific resource ('users in a course'), and a distinct filtering dimension ('by enrollment type') with all five allowed values enumerated. This unambiguously differentiates it from sibling tools like list_courses (which lists courses, not users) and other list tools. The purpose is immediately clear without needing to inspect 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?

The description clearly indicates this tool is for retrieving users of a course and filtering by enrollment type. It implies the need for a course_identifier, likely obtained from list_courses, but does not explicitly state when not to use it or mention alternatives. The context is clear, but there is no explicit exclusion or routing to other tools.

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