compare_llm_responses

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

With no annotations provided, the description carries the full burden and does well by disclosing key behaviors: parallel execution ('sends...in parallel'), default values (Ollama as second agent, specific defaults), error handling (error fields in response), and exception conditions (raises ValueError). It doesn't mention rate limits or authentication needs.

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 appropriately sized and well-structured: purpose statement first, then execution details, followed by parameter explanations, return format, and error conditions. Every sentence adds value with zero wasted text.

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?

Given the tool's complexity (parallel LLM calls with comparison), no annotations, and 0% schema coverage, the description provides complete context. It explains purpose, behavior, all parameters, return format (detailed dictionary structure), and error conditions. The output schema exists but the description still adds useful semantic context.

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 fully compensates by explaining all 4 parameters in detail: 'prompt' (what to send), 'llm_model' (which second model with default), 'temperature' (for both LLMs with default), and 'max_tokens' (maximum tokens with default). It adds meaning beyond the bare schema.

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's purpose with specific verbs ('compare', 'sends') and resources ('Claude and a second agent', 'structured comparison of their responses'). It distinguishes itself from sibling tools by focusing on LLM response comparison rather than media categorization or discovery.

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 provides clear context for when to use this tool ('compare how Claude and a second agent respond to the same prompt'), but doesn't explicitly state when not to use it or mention alternatives. The sibling tools are unrelated (media categorization/discovery), so no direct alternatives are needed.

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