llm_openrouter

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

No annotations exist, so the description must disclose behavior. It states 'Send prompt' implying a read-like operation, but provides no information about authentication, rate limits, cost, or side effects. For a tool that likely makes external API calls, this is insufficient.

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 extremely concise—one sentence. While front-loaded, it omits valuable details that could be added without verbosity. It earns a moderate score for no wasted words but significant under-specification.

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 presence of many sibling LLM tools, no output schema, and no annotations, the description is completely inadequate. It fails to mention that OpenRouter is a model router, how to select models, or what the response looks like.

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 should explain each parameter. It only implicitly covers 'prompt' (by naming it in the description). 'model' and 'max_tokens' are left undefined; the agent has no indication of valid model strings or token limits.

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 'Send prompt to openrouter LLM' clearly indicates the action (send prompt) and the target (openrouter LLM). It distinguishes the tool from siblings by naming a specific provider, but lacks additional context about what OpenRouter offers.

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?

No guidance is provided on when to use this tool versus the many other LLM tools (e.g., llm_anthropic, llm_openai). An agent cannot determine if this is for general queries or specific use cases.

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