list_local_models

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

With no annotations, the description carries the full transparency burden. It mentions the local endpoint and that results reflect availability, but it does not explain error behavior, return format, or what happens if the backend is unreachable. Still, it adds useful context about confirming reachability.

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 two sentences, front-loaded with the core action, and every word adds value. There is no redundancy or filler.

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 zero-parameter, no-output-schema tool, the description is reasonably complete. It states what the tool returns ('model IDs'), the context ('local llama-swap endpoint'), and practical use cases. It could mention edge cases like empty results or connection errors, but these are minor gaps.

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 zero parameters, so the baseline is 4. The description adds no param details, but none are needed since the schema is empty. It correctly communicates that the tool requires no inputs.

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 uses a specific verb ('List') and explicitly identifies the resource ('model IDs available on the local llama-swap endpoint'). It clearly distinguishes itself from the sibling tool 'delegate_code' by focusing on model enumeration rather than code execution.

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 gives explicit use cases: 'pick a model' and 'confirm the backend is reachable.' While it does not mention when not to use it or alternatives, for a simple list operation this is adequate guidance.

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