compare_ollama_responses

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

No annotations are provided, so the description must disclose behavioral traits. It only states a high-level purpose without details on behavior, such as default model selection, response format, error handling, or any side effects. This is insufficient for an agent to predict tool behavior.

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 sentence that is front-loaded with the core purpose. It is concise with no unnecessary words or repetition.

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?

Despite having 3 parameters and no output schema, the description provides only a minimal purpose. It fails to explain how the tool works (e.g., default model behavior, output structure), leaving significant gaps for an agent to correctly invoke the tool. The sibling tools suggest more specialized alternatives, but no cross-referencing is provided.

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 input schema has 100% description coverage for all three parameters. The description does not add new meaning beyond what is already in the schema, so the baseline score of 3 applies. The description's mention of 'multiple models' aligns with the models parameter but does not enhance it.

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 compares responses from multiple Ollama models on the same prompt, with the goal of diverse perspectives. This specific verb+resource combination distinguishes it from the sibling tool consult_ollama, which is for single model queries.

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 for getting diverse perspectives, but provides no explicit guidance on when to use or not use this tool versus alternatives like consult_ollama or sequential_consultation_chain. No exclusions or prerequisites are mentioned.

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