ask_gpt

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

No annotations are provided, so the description must carry the full burden. It mentions that higher reasoning_effort is deeper but slower, and that models have different capabilities (e.g., max effort per model). However, it fails to disclose potential behavioral traits such as token limits, cost implications, or timeout behavior.

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?

The description is dense but every sentence adds value. It front-loads the core purpose, then provides clear guidelines and examples. No redundant or filler content.

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 absence of an output schema, the description does not explain what the tool returns (e.g., plain text response). It covers usage patterns well but lacks details on error handling or maximum response size. Still, it provides sufficient context for an AI agent to use correctly with the sibling tools.

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% (all parameters have descriptions), so baseline is 3. The description adds significant value by explaining model aliases and use cases (sol for reasoning, terra for coding, luna for high-volume), the reasoning_effort scale with model-specific caps, and how to write instructions. This goes beyond the schema's minimal descriptions.

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 tool's purpose: 'Ask an OpenAI model as an expert subagent. ONE tool for everything.' It specifies the key parameters (model, instructions, reasoning_effort) and distinguishes from sibling tools (list_patterns, get_pattern) by noting they are for patterns, not LLM calls.

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?

Provides extensive guidance: when to use fast models (gpt-5.6-luna) for high-volume work, strong models (gpt-5.6-sol) for reasoning, different reasoning_effort levels, and the orchestration patterns (worker vs two-layer-cross-model-expert). Explicitly recommends calling list_patterns/get_pattern first for non-trivial work.

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