list_courses

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

readOnlyHint=true already tells the agent this is a safe read, so the bar is lower; the description still adds real behavior by disclosing the fallback to the local course table when Moodle is not logged in. It does not describe pagination or result size limits, but the fallback note is substantive context beyond the annotation.

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?

Front-loaded with the verb and resource, then two short qualifiers (when to use it, fallback behavior) and the return fields. Every clause carries information and nothing is repeated from structured data.

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?

With no output schema, the description compensates by naming the returned fields (course code, name, identifier), and it covers the login-state edge case. For a zero-parameter listing tool that is complete enough to call correctly.

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 there is nothing for the description to disambiguate; baseline 4 applies. The description correctly does not invent parameter guidance.

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+resource (列出课程/list courses) and names both data sources (Moodle/Ed). It also positions itself as the course-discovery hub that other tools depend on, which distinguishes it from the content-specific siblings like get_moodle_grades or list_moodle_due.

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?

Gives two concrete use conditions: when the user asks what courses exist, and when another tool first needs to resolve a course. No explicit when-not or named alternative is provided, but the triggering contexts are clear.

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