t2000_chat

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

With no annotations, the description covers key behavior: private, billed, requires API key, model defaults, and prompt/messages override logic. It does not mention destructive side effects, which are expected to be absent.

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?

Four sentences with front-loaded purpose and key details. It is concise but could be slightly more structured. No wasted words.

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 5 parameters, no output schema, and no annotations, the description covers core functionality, prerequisites, and behavior. Missing details on return format, error handling, or rate limits, but adequate for a chat tool.

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 is 3. The description adds value by explaining the model default, that prompt is a shorthand for a single user message, and that messages override prompt, going beyond schema info.

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 it runs private inference, is OpenAI-compatible, and mentions billing. It distinguishes from siblings like t2000_models for discovering model ids, providing a specific verb+resource scope.

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 explains when to use (private inference with credit) and prerequisites (T2000_API_KEY). It also contrasts with t2000_models for model IDs, but does not explicitly state when not to use or list alternatives for other tasks.

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