list_conversations

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It describes the return format (sorted by most recent message, includes specific metadata) and mentions pagination via limit/offset, which adds useful context. However, it lacks details on permissions, rate limits, or error handling, which are important for a list operation. The description does not contradict any annotations, as none are given.

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 and concise, with no wasted words. It front-loads the purpose, lists metadata details in bullet points for clarity, and separately documents args and returns. Each sentence adds value, making it easy to scan and understand quickly.

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 moderate complexity (list operation with pagination), no annotations, and an output schema exists (implied by 'Returns'), the description is fairly complete. It covers purpose, parameters, and return structure adequately. However, it could improve by addressing behavioral aspects like permissions or error cases, but the output schema reduces the need to explain return values in detail.

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?

The input schema has 0% description coverage, so the description must compensate. It explicitly documents both parameters ('limit' and 'offset') with their purposes and default values, adding clear meaning beyond the schema. This fully compensates for the schema's lack of descriptions, though it does not provide additional semantic nuances like constraints or examples.

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 the tool's purpose: 'List all conversations with metadata.' It specifies the verb ('List') and resource ('conversations'), and details the metadata included (participant info, contact names, message count, etc.). However, it does not explicitly differentiate from sibling tools like 'get_recent_messages' or 'search_messages', which might have overlapping functions, so it misses full sibling distinction.

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 provides no guidance on when to use this tool versus alternatives. It does not mention sibling tools such as 'get_recent_messages' for recent messages or 'search_messages' for filtered searches, nor does it specify prerequisites or contexts for usage. This lack of comparative guidance leaves the agent without clear direction on tool selection.

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