Fetch the status and latest result payload for a GPT Image 2 task, letting you poll asynchronous image edits or text-to-image generations until they finish.
Fetch the current status and latest result payload for a gpt-image-2 task.
| Name | Required | Description | Default |
|---|---|---|---|
| action | Yes | Asynchronous endpoint the task was created on. | |
| task_id | Yes | Task id returned when the task was created. |
Changes observed during successful MCP inspections.
v0.2.0Input schema / additionalPropertiesRemoved value: -falseInput schema / properties / action / descriptionPrevious value: -"Endpoint the task was created on."New value: +"Asynchronous endpoint the task was created on."v0.1.0Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full behavioral burden. It states that it returns current status and the latest result payload, which is useful, but does not disclose whether the task is complete, what status values mean, error/retry behavior, or auth requirements.
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?
A single, front-loaded sentence with no wasted words. It is efficient, though extremely terse given the gaps in behavioral and usage 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?
With no output schema and no annotations, the description should explain the shape of the returned status and payload and what to do when a task is not yet finished. It only hints at the return contents, leaving meaningful gaps for a polling tool.
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%, so both task_id and action are already documented in the schema, including the enum meanings for action. The description adds no additional semantic detail beyond the schema, so baseline 3 applies.
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 verb (Fetch) and resource (the current status and latest result payload for a gpt-image-2 task), which clearly separates it from siblings like login, edit_image, and text_to_image. It does not explicitly name which sibling it complements (the task-creation endpoints), but the operation is unambiguous.
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?
There is no guidance on when to call this tool, how often to poll, or how it relates to edit_image/text_to_image that create the tasks. The agent must infer that this is the polling companion to those creation tools.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.