List available agy agents

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

No annotations exist, so the description carries the full burden. It usefully discloses the return shape ('raw agy agents output') and the empty-result semantics, but omits whether built-in agents are included, ordering, or failure behavior. Read-only nature is only implied by 'List'.

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 short sentences, front-loaded with purpose and return semantics, with no filler. The terseness comes at some cost in clarity around what 'agy agents output' contains.

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?

For a no-parameter read tool with no output schema, the description covers purpose, return format, and empty-result meaning. Only minor gaps remain, such as agent categorization and ordering, which are unlikely to block correct 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?

Zero parameters, so the baseline of 4 applies. The description does add value by connecting the tool output to the 'agent' argument of other tools, which the empty schema cannot convey.

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 and resource ('List agent profiles') and ties it to a concrete consumer ('for agent argument'), which makes the purpose actionable. It is reasonably distinguishable from list_models, though the domain jargon 'agy agents' is unexplained and the tie to siblings is only implicit.

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?

Usage is implied rather than stated: the agent must infer that this is called to discover valid values for the 'agent' argument. The note that empty output means no custom agent configured is a useful usage signal, but there is no explicit when-to-use/when-not guidance or naming of alternatives.

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