orcarouter_chat

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

No annotations were provided, so the description carries full burden. It discloses single-turn nature, default model behavior, model naming conventions, and automatic token translation for reasoning models. It does not mention return format or error handling, but the covered behaviors are valuable.

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?

Exactly two sentences with no extraneous content. First sentence establishes purpose, second sentence provides key usage details. Front-loaded and 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, 1 required, no output schema, and no annotations, the description covers core behavioral aspects like model selection and token handling. It does not mention return values or error responses, which would improve completeness. Adequate but not thorough.

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% (each parameter has a description). The tool description adds value by explaining model format options and token routing, which are not fully captured in parameter descriptions. However, the schema already provides detailed parameter info, so additional contribution is moderate.

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 verb 'Send' and the resource 'single-turn chat request' to OrcaRouter. It distinguishes from sibling tools like orcarouter_model_card (which likely retrieves model info) by focusing on sending a chat.

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 explains when to use default model vs specifying routers or direct providers, and notes automatic token mapping for OpenAI reasoning models. It lacks explicit when-not-to-use but provides sufficient context for correct invocation.

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