get_workers

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

With no annotations, the description fully discloses behavioral traits: models are verified alive by default (sends real prompt), provider diversity is enforced (at most 1 per provider), ranking by tier then latency, and return format of model_ids.

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 concise with a clear structure: summary line, use case context, then detailed parameter explanations. Every sentence adds value without redundancy.

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 4 parameters, no required ones, and an output schema, the description is complete. It covers purpose, behavior, parameter details, and return value context, leaving no gaps for an AI agent to misinterpret.

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 0%, so the description must add all parameter meaning. The 'Args' section clearly explains each parameter (count, min_tier, free_only, verified) with defaults and semantics, fully compensating for the lack of schema descriptions.

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: 'Get N verified-alive models across N distinct providers, ranked by tier then latency.' It specifies the action, resource, and key constraints, distinguishing it from siblings like 'list_models' or 'get_fastest'.

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 identifies common use cases: 'translation pipelines and batch evaluation' and explains the output is ready for 'run' or 'batch_run'. It provides context for when to use the tool, though it stops short of explicitly stating when not to use it.

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