Zweitmeinung von GPT

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

With no annotations the description carries the full burden, and it does disclose a genuinely important trait: the model sees only the prompt, so context, code and prior attempts must be supplied by the caller. It omits cost/latency expectations, failure behavior, and what the returned answer looks like.

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?

Three tight sentences: identity/route first, fit-for-purpose second, the critical prompt-only caveat last. No filler and no repetition of schema content.

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 four-parameter inference tool with full schema coverage, no output schema and no annotations, the description covers the essentials, including the non-obvious statelessness caveat a caller would otherwise miss. Minor gap: nothing about cost, latency or error behavior.

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?

Schema description coverage is 100%, so all four parameters (prompt, effort, max_tokens, system_prompt) are already documented; baseline 3 applies. The description reinforces the default effort of high and the 'only the prompt is visible' constraint, but adds no syntax or format detail beyond the 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?

Names a concrete verb+resource ('Holt eine Zweitmeinung'), the exact model (openai/gpt-5.6-sol), the effort level and the transport (OpenRouter), so the agent knows precisely what it gets. It differentiates itself by tier ('Günstige Alltagsvariante, keine Eskalation') but never names or contrasts the sibling query_dual_models explicitly.

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?

Clear context for use — a cheap everyday option for a second perspective — plus an explicit exclusion ('keine Eskalation'), which tells the agent when NOT to pick it. It stops short of naming the alternative route to use instead, so routing is only partially resolved.

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