chat

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

No annotations are provided, so the description carries the burden. It states what it does (chat completion, returns reply) but doesn't disclose cost implications, rate limits, or whether message history needs to include prior responses for multi-turn conversations. For a cost-incurring tool, this is a moderate gap.

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 concise sentences that state purpose and return value with zero waste. Efficient and front-loaded.

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 moderately simple 4-param tool with 100% schema coverage, the description is largely sufficient. It could mention that this is a cost-incurring operation for balance tracking (given get_balance/get_usage siblings exist), but the core usage is well covered.

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%, with all four parameters documented (model, messages, max_tokens, temperature). The description adds nothing beyond the schema, so baseline 3 is appropriate given full coverage.

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 uses a specific verb+resource ('Send a chat completion to any Farebox model') and states the return value. It's clear, though it doesn't distinguish itself explicitly from siblings like list_models or call_skill beyond the obvious chat-purpose.

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 implies when to use it (chatting with a model) and the 'any Farebox model' phrasing suggests it works across models. But it doesn't explicitly address when not to use it, or contrast with sibling tools like call_skill (e.g., when a skill is needed).

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