Upscale low-resolution videos with AI super-resolution to create higher-detail output files. Set input and output paths, then choose the scale factor and model.
Upscale video using AI super-resolution.
| Name | Required | Description | Default |
|---|---|---|---|
| model | No | realesrgan | |
| scale | No | ||
| overwrite | No | Explicitly allow replacing existing media. Existing non-media files are protected. | |
| input_path | Yes | ||
| output_path | Yes |
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Changes observed during successful MCP inspections.
v1.16.4Input schema / properties / overwriteAdded value: +{
+ "default": false,
+ "description": "Explicitly allow replacing existing media. Existing non-media files are protected.",
+ "title": "Overwrite",
+ "type": "boolean"
+}v1.16.0Input schema / additionalPropertiesAdded value: +falsev1.5.1Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full behavioral burden, yet it discloses nothing about cost/runtime, supported input formats, whether the input file is modified, or what happens when output_path already exists. The 'overwrite' protection semantics exist only in the schema, never echoed or expanded in the description.
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 front-loaded sentence with zero padding, which is structurally good, but it is terse to the point of under-specification rather than efficient communication for a 5-parameter mutation tool.
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?
An output schema exists so return values need not be described, but for a 5-parameter write operation with no annotations and 20% schema coverage, the description leaves model selection, scale validity, overwrite behavior, and performance expectations entirely undocumented.
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 only 20% (just 'overwrite'), and the description adds nothing about 'model' (default realesrgan), 'scale' (default 2, and what values are legal), or the required input/output paths. 'Super-resolution' hints at scaling but gives no usable semantics for the model or scale parameters.
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?
States a specific verb and resource ('Upscale video') plus the technique ('AI super-resolution'), so the agent knows exactly what operation this performs. It does not differentiate from nearby siblings such as video_resize, video_restoration_plan, or video_stabilize, which is the only thing keeping it from a 5.
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?
There is no guidance on when to choose this over video_resize, video_restoration_plan, or video_filter, and no prerequisites (GPU availability, codec/format limits, typical runtime) are stated. The agent must infer usage entirely from the tool name.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.