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

No annotations are provided, so the description must disclose behavior on its own. It discloses unified multi-provider access and smart auto-routing, but omits response shape, streaming/async behavior, rate limits, or cost implications. This is adequate but not rich.

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 short, front-loaded sentences; every sentence carries value. Avoids restating schema boilerplate and includes a memorable value proposition.

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?

Covers tool purpose, model breadth, and routing strategy in minimal space; 'OpenAI-compatible' hints at response format. However, without output schema it leaves return details and error/cost behavior implicit, which is acceptable for a simple chat-completion tool but not exhaustive.

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?

Input schema covers all four parameters with descriptions, so baseline is 3. The description reiterates the 'auto' option already present in the model parameter schema and adds provider examples, but adds no new semantics for messages, max_tokens, or temperature.

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?

Opening verb 'Send' plus object 'a chat completion' clearly states the action, and 'any model on JarvisClaw through one OpenAI-compatible endpoint' defines the resource and scope. Mentioning GPT/Claude/Gemini/DeepSeek and auto-routing differentiates it from sibling list/search/discovery tools.

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?

Description gives clear context: use this for chat completions across models, with 'auto' routing as a notable option. It implies it supersedes per-provider setup but doesn't explicitly name alternatives or state when not to use it (e.g., for model discovery or cost estimation).

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