check_job_statusPolling-friendly status check for one of your tasks. Returns a slim shape with status, progressPct, and eta so you can poll without refetching the full payload.
| Name | Required | Description | Default |
|---|---|---|---|
| taskId | Yes | Task id to check. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint and idempotentHint, so no contradiction. The description adds useful context beyond annotations by detailing the response shape and the polling-friendly behavior, which is relevant for an agent deciding whether to call this repeatedly.
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 sentences, front-loaded with the core purpose ('Polling-friendly status check'), and every clause adds value. No fluff or repetition of schema details.
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 simple single-parameter tool with no output schema, the description covers the return fields and the polling use case, making it sufficiently complete for the agent to understand the tool's basic behavior. It does not cover error cases or edge conditions, but these are less critical for such a simple read-only operation.
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% for the single parameter, taskId, which is described as 'Task id to check.' The description does not add new parameter details beyond the schema, but it does mention 'one of your tasks' which is a minor contextual reinforcement. Baseline 3 is appropriate given the schema adequately documents the parameter.
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 the verb 'check' and the resource 'job status', and specifies it returns a slim shape with status, progressPct, and eta. It distinguishes itself from siblings by emphasizing the polling-friendly lightweight nature, which differentiates it from heavier status tools like get_video_status.
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 conveys that this tool is for polling scenarios ('Polling-friendly') and explains the benefit ('without refetching the full payload'), providing clear context for when to use it. However, it does not explicitly name alternatives or state when not to use it, so it stops short of full exclusionary guidance.
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.
Several tools occupy nearly identical semantic ground: apply_iphone_realism and apply_ugc both describe casual phone-shot looks, upload_media and upload_reference_asset both accept uploads, and analyze_video overlaps heavily with analyze_video_report. The many apply_* style tools are essentially one tool parameterized by style, so agents can easily select the wrong one.
Most tools follow a clear verb_noun snake_case pattern such as generate_image, list_my_videos, get_editor_run, and upscale_video. A few outliers like voice, talking_avatar_video, and video_to_prompt do not use the same verb-first convention, but they are still readable and do not create significant confusion.
At 55 tools, the surface is far beyond what is appropriate for an MCP server; many of these be collapsed or parameterized, especially the 10 apply_* style wrappers and several overlapping upload/status helpers. Even for a broad media platform, this scale forces a huge context window and makes selecting the right tool impractical.
The surface covers generation, media display, video analysis, and Editor workflows well, but there are obvious gaps in library lifecycle management: move_asset and create_folder are referenced in tool descriptions without being exposed, and there is no clean way to delete or reorganize media assets. Agents following the descriptions will try to call tools that do not exist.