cancel_my_taskStop one of your generation tasks by task id — works on queued AND running tasks. Already-saved images stay in your library; nothing is deleted or refunded. Returns how many images were saved out of how many you requested.
| Name | Required | Description | Default |
|---|---|---|---|
| taskId | Yes | Task id from generate_image or list_my_tasks. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description adds important behavioral details beyond annotations: 'Already-saved images stay in your library; nothing is deleted or refunded' and 'Returns how many images were saved out of how many you requested.' This goes beyond the simple mutation/idempotent hints and clarifies side effects and return value.
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: the first states purpose and scope, the second covers side effects and return format. Every sentence adds value with no redundancy, and it is front-loaded with the action.
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 single-parameter mutation tool with no output schema, the description fully explains behavior, side effects, and return value. Combined with the idempotentHint annotation and schema parameter description, it leaves no important gaps for the agent.
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?
The schema already provides 100% coverage for the single taskId parameter with its own description ('Task id from generate_image or list_my_tasks'). The tool description does not add additional semantic meaning beyond restating 'by task id', so it meets the baseline for comprehensive schema coverage.
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 'Stop one of your generation tasks by task id' with a specific verb and resource, and clarifies it works on queued AND running tasks. This clearly distinguishes it from sibling tools like generate_image or 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?
It explicitly specifies when the tool applies ('queued AND running tasks'), providing clear context for its use. It does not mention alternatives or exclusions, but the scenario is well-defined enough for an agent to decide when to invoke it.
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.