Multi-Modell-Anfrage

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

Describes parallel query behavior, return of multiple responses, optional synthesis, and default system prompt. No annotations provided, so description carries full burden. It is transparent about the main actions and does not contradict annotations (none exist). Could mention auth or rate limits, but overall sufficient for a read-like operation.

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?

Two sentences, no fluff. Each sentence adds critical information: first on core functionality, second on options and defaults. Highly efficient.

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 6 parameters, no output schema, and no annotations, the description covers the main flow (parallel, multiple models, synthesis, defaults). It could detail the return format (e.g., array of responses), but for a multi-model query tool, it is sufficiently complete.

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%, baseline 3. Description adds value by specifying default models (anthropic/claude-opus-4.8, openai/gpt-5.5), mentioning 'parallel' execution, and describing the standard system prompt behavior (6-8 paragraphs), which is absent in the schema parameter descriptions.

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?

Clearly states it sends a prompt in parallel to multiple models via OpenRouter and returns responses side by side with optional synthesis. Verb 'schickt' and resource 'Modelle via OpenRouter' are specific and distinct. No sibling tools exist, so no differentiation needed.

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?

Implies usage for comparing multiple model responses or obtaining a synthesized output. Mentions default models, default system prompt, and optional synthesis. Lacks explicit when-not or alternatives, but given no siblings, the context is clear enough.

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