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

Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, so the safety profile is fully covered. The description adds the 'most recently active first' ordering and the scope of 'user's own connected WhatsApp account,' which are useful behavioral details. However, it does not disclose response structure, pagination behavior, or whether archived/deleted chats are included, so it adds moderate but not rich behavioral context.

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, each earning its place: the first states the action and ordering, the second explains when to use it, and the third routes to an alternative. The key scoping phrase ('most recently active first') is front-loaded. Zero 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?

The tool has only two optional parameters and no output schema, so the description does not need to explain return values. It covers the main usage scenario, the alternative for a different intent, and the account_id edge case is handled in the schema. A small gap is that it does not distinguish from messaging_list_chats or mention reading/sending counterparts, but the provider-specific name largely resolves that.

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 exactly 50%: account_id has a thorough description covering multi-account behavior and how to choose the ID, while limit has only type/numeric constraints. The tool description itself does not add parameter detail, but the account_id schema carries significant meaning. Since half the parameters are effectively described (account_id) and limit is self-explanatory with min/max bounds, this is adequate but not exceptional.

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 specific verb and resource: 'List WhatsApp chats from the user's own connected WhatsApp account, most recently active first.' It further clarifies the primary use case ('find a chat_id before reading or sending when the user identifies a conversation by person/name rather than ID') and explicitly contrasts with messaging_list_recent_messages. This clearly distinguishes it from sibling tools despite the many similar list_conversations tools.

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 gives both a positive trigger ('Use to find a chat_id before reading or sending when the user identifies a conversation by person/name rather than ID') and a negative trigger with an explicit alternative ('For 'what did I receive since X' use messaging_list_recent_messages'). This is explicit when/when-not guidance with a named sibling, which is exactly what the rubric rewards.

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