Chat via Intelliverse Router

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

With no annotations, the description takes full responsibility. It discloses that model is optional and that default routing occurs based on plan tier, and warns against naming arbitrary LLMs. This adds behavioral context beyond the schema.

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, front-loaded with purpose, no wasted words. Each sentence earns its place: first states action, second provides critical parameter usage guidance.

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 simple tool with 3 parameters and no output schema, the description covers the key behavioral nuance (model routing). It could mention expected response format, but basic use is adequately served.

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 coverage is 100%, so baseline 3. The description reinforces schema's 'tier alias' explanation and adds a warning, but does not introduce new parameter semantic information beyond what the schema already provides.

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 'Send a chat completion,' identifying a specific verb ('Send') and resource ('chat completion'). It differentiates from sibling tools like kb_chat by focusing on general chat without knowledge base integration.

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 provides explicit guidance on the model parameter ('Never name an LLM — omit model or pass specific tiers'), but lacks guidance on when to use this tool versus siblings like kb_chat or list_models.

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