chatgpt_generate_image

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

Beyond annotations, it discloses that generation uses ChatGPT subscription limits, returns a background job, and promises no image until completion. It does not cover failure modes, rate-limit specifics, or output retrieval beyond polling chatgpt_job.

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 and appropriately sized. The final sentence about no promised image slightly overlaps with the polling instruction, but it reinforces an important async expectation.

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 an async image-generation tool with no output schema, it covers the essential workflow: generate/edit, prompt content, background job, and polling. It could be more complete by mentioning how to retrieve the resulting image or referencing chatgpt_read_image.

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 all parameters are already documented. The description adds useful prompt guidance about describing size, composition, transparency, and edits, but it does not explain model, conversation_id, or reference_images beyond what the schema provides.

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 uses a specific verb and resource: generate or edit images. It distinguishes itself from text siblings by naming image generation and editing, and it points to the async workflow via chatgpt_job.

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 clearly establishes the tool for image generation/editing and tells the agent to poll chatgpt_job after invocation. It lacks explicit when-not guidance or a direct comparison to sibling tools like chatgpt_ask or chatgpt_read_image.

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