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

Annotations already declare readOnly/idempotent/non-destructive, and the description adds substantial context beyond them: newest-first ordering, a hard cap of 50, the three source values (ai/dashboard/public_api) with their meaning, and the exclusion of ai_paused_until as not a real switch. That is genuine behavioral disclosure an agent could not infer.

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 lead sentence is front-loaded and every subsequent clause adds real information (limit, ordering, source taxonomy, exclusion). It is dense in a single block and the nested parenthetical for the `ai` source is slightly hard to parse, but nothing is wasted.

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?

There is no output schema, so the description must describe the return shape — and it does: the value switched to, the origin with reason, the teammate, and the timestamp. Combined with the limit and ordering, an agent knows exactly what to expect from a call.

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% and the two parameters carry their own 404/403 semantics, so the schema does the heavy lifting. The description adds nothing about agent_id or conversation_id, which is acceptable at full coverage but not above baseline.

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?

States a specific verb (list) and resource (AI switch events for a conversation), and the scope is precise enough to separate it from resume_conversation_ai, which performs a switch rather than reporting one. An agent can tell what it retrieves without opening the schema.

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?

"Use it to tell why a conversation's AI is off" gives an explicit triggering situation, and the closing note excludes the timed pause (ai_paused_until) as a non-case. It never names resume_conversation_ai as the alternative action tool, so the when-not side is only partially covered.

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