Upscale videos from public URLs using AI models. Choose motion or motion-x and scale 2x or 4x to enhance video resolution.
Submit a video upscale job from a public URL. Models: motion, motion-x.
| Name | Required | Description | Default |
|---|---|---|---|
| model | No | Video model. Default motion. | |
| scale | No | Scale multiplier. Default 4. | |
| video_url | Yes | Public HTTPS URL of the video |
Changes observed during successful MCP inspections.
v0.2.0Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden. 'Submit a job' implies asynchronous behavior, but the description does not disclose that it likely returns a job ID, that it consumes credits, or that results must be polled with get_job. This is a meaningful transparency gap for a job-submitting 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?
The description is two short sentences with no filler. It front-loads the action and resource, then lists model options. Every word 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?
With no output schema and no annotations, the description should explain what happens after submission. It omits the async nature, the likely job ID return value, the need to poll with get_job, and potential credit usage. These are essential for an agent to correctly handle the tool's result.
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 documents all three parameters with descriptions, enums, and defaults, so description-level parameter information is not strictly needed. The description adds 'Models: motion, motion-x,' but this only repeats schema content. Baseline 3 is appropriate given 100% 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 a specific verb ('Submit') and resource ('video upscale job'), and clarifies the required input ('public URL'). This clearly distinguishes it from the sibling image tool upres_upscale_image and other upres 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 description gives clear context: this is for video upscaling jobs from a public URL, with available models listed. It does not explicitly name alternatives or exclusions, but the video-vs-image distinction is obvious from the tool name and description, so the usage context is clear.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.