list_models

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

With no annotations, the description carries the full behavioral burden. It implies a read operation but never states that it is side-effect free, whether it reflects live vs cached configuration, or what the returned structure looks like. Only the adjective 'configured' hints at the data source.

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?

A single, front-loaded sentence with the verb first and no filler. It is slightly compressed (a fragment rather than a sentence), but every word contributes.

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 simple zero-parameter read tool this is borderline adequate, and listing 'tiers and declared capabilities' partially signals the return content. Without an output schema, though, the agent still does not know the response shape or how it differs from discover_models.

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 takes zero parameters, so there is nothing for the description to disambiguate. Baseline of 4 applies for a parameterless listing 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?

States a clear verb (list) and enumerates the resources returned (routing targets, tiers, declared capabilities), so the read intent is unambiguous. However, it does not distinguish itself from the very similar siblings discover_models and provider_inventory, leaving the agent to guess which one surfaces model/routing information. Purpose is legible but not sibling-differentiated.

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?

There is no guidance on when to call this versus discover_models or provider_inventory, nor any prerequisite or state context. The word 'configured' hints at configuration-state vs live discovery, but this is left for the agent to infer.

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