ask_gpt

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

With no annotations, the description carries full burden. It mentions treating output as a hypothesis, hinting at potential inaccuracy. However, it does not disclose error handling, rate limits, cost implications, or what happens with invalid model IDs.

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 well-structured sentences. The first states the core action and key requirements. The second provides model recommendations and a caveat. Every sentence earns its place.

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?

The description lacks output format details (no output schema). It does not explain what the tool returns (e.g., text, JSON). For a 5-param tool with no output schema, this is a significant gap.

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%, baseline 3. The description adds value by linking model IDs to use cases (e.g., gpt-5.5 for deep reasoning) and suggesting reasoning_effort coupling, going beyond the schema.

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 asks a GPT model via ChatGPT subscription. The verb 'ask' and resource 'GPT model' are specific. Sibling tools (check_usage, get_pattern, list_patterns) have distinct purposes, so no confusion.

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 explicitly says you must choose model and write instructions yourself, and provides model recommendations with use cases. It lacks explicit when-not-to-use or alternatives, but the context is clear and practical.

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