List local models

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

With no annotations provided, the description carries the burden of behavioral disclosure. It does not detail what happens if no models are found, whether it checks all configured runtimes or just the first, or if any network requests are made. This absence of behavioral context is a gap, but the description avoids contradicting any annotations since none exist.

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 a single, well-structured sentence that is appropriately concise. It front-loads the key action ('Discover models') and immediately clarifies the context (localhost model runtimes) with concrete examples. There is no unnecessary information.

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 tool with zero parameters and no output schema, the description is quite complete. It clearly defines the tool's scope and expected behavior. The only potential gap is the lack of detail on how the discovery works or what happens in edge cases, but for a simple discovery tool, this is sufficient.

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?

Since the tool has zero parameters, the description does not need to add parameter semantics. The schema coverage is 100% (trivially, as there are no properties), but the description adds value by specifying the scope (localhost runtimes) and examples, which is useful for the agent to understand what 'discover models' means.

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: to discover models from configured localhost model runtimes. It specifies the resource (local models) and provides concrete examples of supported runtimes (Ollama, LM Studio, LocalAI), which effectively distinguishes it from sibling tools that deal with endpoint configuration and description.

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 implies usage when you need to discover available models from local runtimes, which provides clear context. It does not explicitly state when NOT to use it or mention alternatives such as get_model_endpoint_config, but given the simple nature and sibling names, the usage is clear enough for an agent to select it appropriately.

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