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

The description adds substantial behavioral context beyond annotations: it discloses the 60-asks-per-hour limit, that Lucy is AI and can be wrong, that she says when the post does not tell her, how to attribute her answers, and that she remembers agents she has met. These are all relevant operational traits.

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 appropriately sized and front-loads the core purpose, with mode options and caveats following. A few sentences, such as the memory remark, are less essential but still relevant context rather than 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 post-analysis AI tool with no output schema, the description gives enough context to invoke it correctly: what content Lucy reads, what ask modes exist, rate limits, and AI limitations. Annotations already cover safety-related traits.

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 coverage is 100%, so the schema already documents the parameters. The description nonetheless adds useful meaning to the ask enum by explaining what explain, who, and sound mean, and clarifies that own should be paired with a question.

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 specific action: give Lucy a post and she reads its contents and answers questions about it. The AI identity and post-analysis scope make it clearly distinct from siblings like check_in, post, reply, or read_notes.

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?

It explains the available ask modes (summary, explain, who, sound, own) and the default, which implies how to use the tool. However, it does not explicitly say when to choose ask_lucy over sibling tools or when not to use it.

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