get_generation_statusCheck a generation started with generate_video (or a generate_image that timed out). Returns running, completed (with output URLs) or failed (refunded).
| Name | Required | Description | Default |
|---|---|---|---|
| request_id | Yes | The request_id returned by the generate tool. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden. It usefully discloses the tool's read-only nature ('Check') and the three possible outcomes (running, completed with output URLs, failed/refunded). This goes beyond the schema and gives agents concrete expectations. It doesn't cover polling cadence or latency, but that's not essential for a status-check tool.
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 sentence that packs purpose, applicable source tools, and return outcomes with zero filler. The most important information is front-loaded, and every phrase earns its place.
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?
The description is sufficient for a simple status-polling tool. It clarifies the source of request_id, the lifecycle states, that output URLs appear on completion, and that failures result in a refund. Without an output schema, these details provide the necessary context. It omits only minor guidance on polling intervals or error handling, which are less critical.
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% and the request_id parameter is already well-documented as 'The request_id returned by the generate tool.' The description reinforces this by mentioning generate_video/generate_image, but adds no new semantic detail beyond the schema. The baseline of 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 opens with a specific verb ('Check') and a clear resource ('a generation started with generate_video...'), and explicitly names the sibling tools it relates to. This makes it immediately distinguishable from generate_image, generate_video, and list_models without needing to inspect schemas.
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 usage context: use this tool to check the status of a generation started by generate_video or a timed-out generate_image. It doesn't list 'when not to use' or alternatives, but the precondition is clear enough to prevent misuse.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
Each tool targets a distinct purpose: image generation, video generation, status polling, and model listing. There is no functional overlap between them; the async nature of video generation and the status tool clearly complement each other without ambiguity.
All tool names follow a consistent verb_noun snake_case pattern: generate_image, generate_video, get_generation_status, list_models. The verb prefixes (generate, get, list) map predictably to actions and resources.
With 4 tools, the set is tightly scoped for a media generation server. Each tool serves a necessary role: creating images, creating videos, checking async status, and discovering models. No redundant tools exist.
The domain is AI media generation, and the lifecycle is fully covered: start an image generation, start a video generation, poll for status/results, and list available models. There are no dead ends; even timeout scenarios are addressed via get_generation_status.