Poll and retrieve a ChatGPT generation job's response text, conversation ID, and image paths; completed small images return inline, so check running jobs instead of resubmitting requests.
Retrieve a generation job, response text, conversation ID, and image paths. Completed small images are returned inline. Poll running jobs; do not resubmit the original request.
| Name | Required | Description | Default |
|---|---|---|---|
| job_id | Yes | ||
| wait_seconds | No | ||
| include_images | No |
Changes observed during successful MCP inspections.
v0.1.0Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Beyond the readOnlyHint annotation, the description adds useful behavioral details: completed small images are returned inline, and running jobs should be polled rather than resubmitted. These points clarify runtime behavior that the annotations alone do not convey. It still does not mention rate limits or authentication requirements, but the added context is meaningful.
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 two compact sentences with zero redundancy. It front-loads the retrieval purpose and then adds operational constraints and return behavior in an efficient sequence.
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, the description appropriately mentions returned fields and inline images. However, for a tool with three parameters at 0% schema description coverage, the missing explanation of wait_seconds and include_images leaves the definition incomplete for an agent to invoke it optimally.
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 0%, so the description must compensate but does not. It never mentions the job_id, wait_seconds, or include_images parameters directly, leaving wait_seconds and include_images completely unexplained. The inline image statement loosely touches include_images behavior but is not explicit parameter guidance.
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 that this tool retrieves a generation job along with its response text, conversation ID, and image paths. It distinguishes itself from a generic status tool by specifying the job-specific output. However, it does not explicitly contrast itself with the sibling chatgpt_status or chatgpt_ask tools.
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 phrase 'Poll running jobs; do not resubmit the original request' gives a clear usage context for this tool. However, it does not name alternative tools or explain when to use chatgpt_status versus chatgpt_job, leaving some ambiguity for an agent deciding between siblings.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.