gpt_search

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

With no annotations, the description fully discloses behavioral traits: it sends the prompt to ChatGPT, handles file output with directory creation, details the return_output default logic, and explains output_json post-processing including failure recovery. This is comprehensive and leaves no ambiguity.

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 well-structured with an Args section and clear parameter explanations. It is informative without being verbose, though some redundancy (e.g., repeating 'default') could be trimmed. Overall, it effectively communicates key details.

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 tool's complexity (5 parameters, multiple behaviors) and the presence of an output schema (not shown but indicated), the description covers all necessary aspects: parameter usage, alternatives, defaults, and error handling. It is complete enough for an AI agent to use 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?

The input schema has 0% description coverage, so the description carries full burden. It provides detailed semantics for all five parameters: query vs. prompt_file exclusivity, output_file with directory creation, return_output defaulting based on output_file, and output_json with fallback behavior. This significantly enhances schema understanding.

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 'Search the web or research a topic using ChatGPT.' It identifies the tool's core function and the two input options (query or prompt_file). However, it does not explicitly differentiate from sibling tools like gpt_image_gen or gpt_search_batch, though the purpose is distinct enough.

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 provides explicit guidance on parameter usage: 'Provide either `query` or `prompt_file`.' It also explains default behaviors for return_output and output_json. It lacks explicit when-to-use vs. alternatives, but the context from sibling names and the detailed parameter logic compensates.

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