list_conversations

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

With no annotations, the description carries full behavioral burden. It discloses important traits: the return structure (fetched, returned, platforms_queried, platforms_failed, conversations), per-conversation fields, and a caveat that fetched is not the total but the actual count due to quota limits. It stops short of explicitly stating auth requirements or confirming read-only behavior, but for a list operation this is sufficient.

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 well-structured with labeled sections (用途, 呼叫的 Shopline API, 回傳結構) and front-loaded with purpose. It is slightly longer than necessary due to the detailed return structure, but every section adds value and it remains focused.

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 tool's simplicity (2 optional params, no output schema), the description is thorough: it explains the purpose, API call, return structure, and a notable behavioral nuance about quota limits. It even directs the user to the appropriate next-step tool, making it complete for successful invocation.

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 100%: both platform and max_results have detailed descriptions in the schema, including enum-like values and default behavior. The tool description adds some context via the API endpoint and return structure, but does not meaningfully enhance parameter understanding beyond what the schema already provides.

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 opens with a clear verb+resource statement ('取得客服對話列表' – get customer service conversation list), then explains it provides a summary list of all conversations. It explicitly distinguishes itself from the sibling tool get_conversation_messages by noting that full message history requires that tool.

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 explicitly states the use case: to browse conversation summaries and identify which conversations need further inspection. It then provides a direct call-to-action to use get_conversation_messages for full chat history, making the when-to-use and alternative clear.

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