get_conversation_messages

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 the API endpoint (GET /v1/conversations/{conversation_id}/messages) and the return structure (dict with conversation_id, total_found, returned, messages[] including fields like id, sender_type, content, message_type, created_at). However, it does not mention potential side effects, authentication requirements, rate limits, or error behavior, leaving some gaps for a tool with no 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 efficiently structured with clear sections: 【用途】, 【呼叫的 Shopline API】, 【回傳結構】. It is concise, using bullet points for the return fields, and every sentence adds meaning. No wasted words.

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 only 2 parameters (one required), no output schema, and no annotations, the description provides a complete picture: it explains the purpose, the source of the required parameter, the API call, and the full return structure with field details. The agent has all necessary information to invoke and interpret the tool.

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 baseline is 3. The description adds value by explaining that conversation_id comes from list_conversations and that max_results has a default of 50. This extra context helps the agent understand parameter origins and defaults, going beyond the schema's bare descriptions.

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 '取得指定對話的完整訊息記錄' (get complete message records of a specified conversation), uses a specific verb (取得) and resource (對話訊息), and lists concrete use cases (了解客戶問題脈絡、審核客服回應品質、追蹤訂單諮詢處理進度). It distinguishes itself from sibling tools like list_conversations or get_order_detail by focusing on messages within a conversation.

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 instructs that the conversation ID comes from list_conversations ('對話 ID 從 list_conversations 取得') and states when to use the tool (查閱特定客服對話的所有聊天訊息). While it does not list when not to use it or mention alternatives, the guidance is clear and contextually sufficient.

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