list_supported_models

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

No annotations are provided, so the description carries full burden for behavioral disclosure. It states the return format ('List of supported model IDs'), which is helpful, but doesn't describe other behavioral traits like whether the list is static or dynamic, if there are rate limits, authentication requirements, or how the data is structured. For a tool with zero annotation coverage, this leaves significant gaps in understanding operational characteristics.

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 extremely concise and well-structured: one sentence states the purpose, and a second clarifies the return value. Every sentence earns its place by providing essential information without redundancy. It's front-loaded with the core functionality and efficiently communicates necessary details.

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 simplicity (0 parameters, read-only operation) and the presence of an output schema (which handles return value documentation), the description is reasonably complete for basic use. However, with no annotations and siblings that suggest a translation workflow, it could benefit from more context about how this tool fits into that workflow or any behavioral constraints. The description covers the minimum viable information but lacks depth for optimal agent guidance.

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 tool has 0 parameters with 100% schema description coverage, so the schema already fully documents the lack of inputs. The description doesn't need to compensate for any parameter gaps. It appropriately doesn't mention parameters, focusing instead on the output. This meets the baseline expectation for a parameterless tool.

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 supported AWS Bedrock models for translation.' This specifies the verb ('List'), resource ('supported AWS Bedrock models'), and domain context ('for translation'). It distinguishes from siblings like 'list_supported_languages' by focusing on models rather than languages. However, it doesn't explicitly differentiate from other list/query tools beyond the domain specificity.

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 doesn't mention prerequisites, timing considerations, or how it relates to sibling tools like 'translate_powerpoint' or 'translate_specific_slides' that might depend on model selection. The agent must infer usage from the purpose alone without explicit direction.

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