ask_model

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

No annotations are provided, so the description carries full behavioral transparency burden. It only states the intent ('Ask a question') without disclosing return format, streaming behavior, error handling, permissions, or side effects, forcing the agent to guess.

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 a clear, front-loaded purpose statement followed by a compact Args list. It contains no filler or redundancy, making it easy to parse quickly.

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 two-parameter tool, the description and Args are mostly adequate, but the absence of any mention of the return value (e.g., the model's generated response) is notable because there is no output schema. It also lacks error or availability information, leaving some ambiguity for the agent.

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?

Although the schema has no descriptions, the description includes an Args block that clearly explains both parameters: 'model' is the model name with an example ('llama2'), and 'question' is the prompt to ask. This fully compensates for the schema gap and adds meaningful semantic detail.

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 action ('Ask a question') and a resource ('specific Ollama model'), making the tool's purpose immediately clear. It also naturally distinguishes itself from sibling tools like list_models and show_model, which are about listing and viewing models rather than interacting with them.

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 no explicit guidance on when to use this tool versus list_models or show_model. There are no conditions, prerequisites, or exclusions, leaving the agent to infer usage solely from the tool name.

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