list_modules

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

No annotations are provided, so the description carries the full burden. It discloses that the tool returns module IDs, titles, descriptions, and tags, and supports filtering. It does not mention pagination or limits, but for a list tool this is acceptable.

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 sentences with no redundancy: first states core purpose, second mentions filters, third describes output. Information is front-loaded and every sentence adds value.

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, the description need not detail return values. It covers the main behavior (listing with filters) adequately. Missing details like result ordering or default sorting are minor gaps.

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 add meaning. It explains that 'session', 'tag', and 'gpu_only' are filters, but does not specify types or formats (e.g., tag free-text, session integer). The parameter names are clear, but additional detail would improve clarity.

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 clearly states 'List all modules' with a specific scope ('dataflowr Deep Learning DIY course'), providing a verb and resource. It distinguishes from sibling tools like 'get_module' (single module) and 'search_modules' (search-based).

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 indicates when to use filters ('Can be filtered by session number, tag, or GPU requirement'), implying optional filtering. However, it does not explicitly exclude use cases or suggest alternatives like 'search_modules' for text-based queries.

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