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

No annotations are provided, so the description carries the burden and does disclose the alias-to-ID mapping purpose and that it returns available models. It does not, however, describe the return shape (no output schema) or note that some aliases may not exist for every modality.

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 first two sentences are tight and front-loaded, but the long list of ~30 aliases is bulky and largely redundant for selecting or invoking the tool. It earns partial credit for utility, but the enumeration bloats the definition.

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 one-parameter lookup tool with full schema coverage, the description is complete enough to select and call it. Missing only a note about what the response includes (model IDs vs. metadata), which is minor given no output schema is required to be described.

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 coverage is 100% and modality is an enum already fully self-documenting, so the description adds little beyond listing the four modality values inline. Baseline 3 is appropriate when the schema does the heavy lifting.

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 (Get) and resource (available Sequencer AI models), scoped to a modality. Clearly distinguishes itself from siblings like get_sequencer_capabilities or get_available_tools by focusing on model names/IDs for generation.

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?

Explicitly says when to use it: when the user asks what models are available or names an alias and you need the exact model ID. That is a precise, actionable trigger tied to a concrete task.

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