List local models

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

With no annotations provided, the description carries the full burden. It discloses that the tool lists models on disk and indicates which is used by default, which is genuinely useful behavioral context. However, it doesn't mention performance, whether it hits the network, or the structure of the returned listing. For a read-only list operation, this is adequate but not rich.

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 with zero wasted words. It front-loads the core purpose and then adds a concrete usage trigger. Every sentence earns its place.

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 read-only listing tool with no output schema, the description covers purpose, what the output contains (sizes, default), and when to use it. It could add a bit more about the returned data format or fields, but the description is largely sufficient for this simple tool type.

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 0 parameters and 100% schema coverage, so the baseline per the rubric is 4. The description adds meaning about what the output provides (sizes, default model), orients the agent on what information is returned, and there are no parameters requiring explanation.

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 what the tool does: list models on disk with sizes and the server's default model. It uses a specific verb ('List') with a clear resource ('local models') and distinct output details. It also distinguishes its purpose from siblings through the 'default' model note and the usage context about checking model existence.

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 an explicit usage context: 'Call it when a request names a model you are not sure exists.' This tells the agent when to invoke it. It doesn't explicitly name alternatives or exclusions, but the sibling tools (local_ask, local_classify) serve clearly different purposes, so the available guidance is reasonably complete for this simple listing tool.

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