chatgpt_generate_batch

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

With no annotations provided, the description carries the full behavioral disclosure burden. It explains the fire-all-then-collect parallel strategy, the fact that it saves to disk, that an authenticated session is required, and the per-job result structure. This is substantial and accurate behavioral transparency.

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 front-loaded with the core action and parallel behavior, then covers consistency, authentication, and return values. There is some redundancy between 'IN PARALLEL', 'one tab per job, fire-all-then-collect', and 'Submits every job first WITHOUT waiting', but each sentence still contributes useful detail.

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 complex batch tool with no annotations and no output schema, the description is complete: it covers authentication prerequisites, parallel execution semantics, consistency-related parameters, and return values. An agent has enough information to invoke the tool correctly and understand what will happen.

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 the baseline is 3. The description adds meaningful semantics beyond the schema by explaining that `shared_image_paths` are uploaded to EVERY tab and `style_suffix` is appended to every prompt for consistency. It also clarifies `jobs` as per-image jobs with prompts and output paths.

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 action ('Generate MANY images on chatgpt.com IN PARALLEL') with a clear resource (chatgpt.com) and outcome (save each to disk). It also differentiates itself from the single-image sibling `chatgpt_generate_image` through the emphasis on batching and parallelism.

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 gives clear context for when to use it: generating many images in parallel and saving results to disk. It provides conditional guidance for consistent feeds using `shared_image_paths` and `style_suffix`, plus a prerequisite ('Requires an authenticated chatgpt.com session'). It does not explicitly name alternatives or state when not to use it, but the use case is clear enough.

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