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

The description adds rich behavior beyond the annotations: fixed price derived from max_tokens, unused tokens not refunded, a 20s server-side timeout with automatic 2nd-provider retry, no charge if both fail, and a recommended 30s client timeout. This is exactly the kind of cost/latency/retry context an agent needs for a paid call.

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?

It is dense but front-loaded: name and model come first, then pricing, then timeout/retry. Each sentence carries a distinct fact (price model, retry policy, sample endpoint), with only mild over-packing.

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?

With an output schema present, return values need no explanation. The description covers the cost model, timeout budget, retry behavior, and auth posture (no API key), leaving nothing an agent needs to invoke it correctly.

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 both parameters are already documented, including max_tokens bounding the price ceiling. The description restates that pricing derives from max_tokens but adds no syntax or format detail beyond the schema, so the baseline of 3 applies.

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 states a specific verb+resource: 'OpenAI-format chat completions on openai/gpt-5.4-mini'. The exact model name implicitly separates it from llm_claude_sonnet and llm_llama, so an agent can infer the routing difference, though no sibling is named explicitly.

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 gives operational hints (pay-per-call, no API key, sample endpoint) but never says when to choose this model over the sibling llm_* or ask_model tools. No condition selects gpt-mini versus claude-sonnet or llama, leaving the routing decision to inference.

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