Ask local model

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

Beyond the readOnlyHint annotation, the description explains a nuanced default behavior: model=None queries /v1/models to reuse whatever model is already loaded, avoiding a model swap or extra RAM usage. This is valuable behavioral context not available in the schema or annotations.

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 front-loaded with the primary purpose and then provides a detailed, relevant explanation of the model default. It is slightly dense and runs on in places, but every sentence contributes useful 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 read-only query tool with an output schema, the description covers the main usage context and the key model-selection behavior. It could add notes on error handling or timeout behavior, but those are less critical given the output schema and simple operation.

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?

With 0% schema description coverage, the description carries more responsibility and does explain the model parameter in depth, including its default and override behavior. However, it does not add meaningful semantics for prompt, timeout, or base_url, leaving those to the schema/defaults.

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 action ('Short query to the local model') and the intended use case ('quick, simple decisions/classification inside a pipeline'). It also references the sibling build_prompts tool contextually, which helps orient the agent, though it does not explicitly contrast itself with alternatives.

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?

It gives concrete usage guidance: 'Use with for_each after build_prompts to ask about many items at once.' This clearly places the tool in a pipeline workflow and implies when it is appropriate, but it does not state exclusions or name when a different tool should be used instead.

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