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

The annotations already declare readOnlyHint=true, so the description's job is lighter. It adds the non-streaming behavior and the fact that it works with any text model, providing context beyond the annotations without contradiction.

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 total: the first states the core action and scope, the second lists use cases and a behavioral note. No filler, front-loaded with the verb, and every sentence serves a purpose.

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 readOnlyHint annotation, full parameter schema, and an output schema, the description provides sufficient context. The non-streaming note and model family list add useful detail beyond what structured data provides.

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?

The input schema has 100% coverage with detailed descriptions for all 6 parameters. The tool description does not add parameter-specific guidance beyond referring to 'conversation' and 'model', so the baseline of 3 is appropriate.

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 action ('Send a conversation... and get the reply') and the resource ('any text model available through CCAPI'), with a specific list of model families. It distinguishes itself from siblings like list_models and get_balance by focusing on the core chat completion functionality.

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?

It explicitly lists three concrete use cases (second opinion, bulk work, reaching unconfigured models) and notes that responses are non-streaming. However, it does not explicitly state when not to use the tool or how it differs from alternatives, though the use cases largely imply this.

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