Kuro

Extract a video frame

extract_video_frame

Save the first or last decoded frame of an owned video as a new image generation. Costs no credits; uses account storage. get_generation waits for the result. To go longer than one clip, carry the last frame into the next generate_clip, then join_clips. Not for speech: each line of a talking head starts from the same portrait, because chaining frames drifts the face.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`source`	Yes
`position`	No		first

Output Schema

TableJSON Schema

Name	Required	Description	Default
`message`	Yes
`generation`	Yes

Schema Changelog

Changes observed during successful MCP inspections.

Changed2 schema fields changedOct 21, 2026

addedOutput schema / properties / generation / properties / ready_in_seconds
```
Added value: +{
+  "type": [
+    "number",
+    "null"
+  ]
+}
```

changedOutput schema / properties / generation / required

Previous value: -[
-  "generation_id",
-  "kind",
-  "status",
-  "model",
-  "prompt",
-  "aspect_ratio",
-  "refined_from",
-  "caption_style",
-  "output_url",
-  "credits_cost",
-  "error",
-  "vendor_phase",
-  "stalled",
-  "created_at",
-  "app_url"
-]New value: +[
+  "generation_id",
+  "kind",
+  "status",
+  "model",
+  "prompt",
+  "aspect_ratio",
+  "refined_from",
+  "caption_style",
+  "output_url",
+  "credits_cost",
+  "error",
+  "vendor_phase",
+  "stalled",
+  "ready_in_seconds",
+  "created_at",
+  "app_url"
+]

First observedOct 15, 2026

TDQS

A4.7/5.0

Behavior5/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already indicate this is a non-read-only, non-destructive, non-idempotent operation. The description adds important behavioral context: it costs no credits, uses account storage, produces a new image generation, and explains the face-drift limitation for talking-head workflows.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is front-loaded with the core action and output, then adds cost, async behavior, chaining guidance, and the speech exclusion. Every sentence contributes useful information without repetition.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given annotations, an output schema, and two parameters, the description covers the action, output, cost, storage use, async retrieval, chaining workflow, and key exclusion. It gives an agent enough context to select and invoke the tool correctly.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

With 0% schema description coverage, the description must compensate. It clarifies that the source is an owned video and that position is first or last, but it does not explain the source object's upload_id versus generation_id structure or the default position behavior.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description states a specific verb and resource: save the first or last decoded frame of an owned video as a new image generation. It clearly distinguishes the output as an image generation and scopes the input to owned videos, making the tool identifiable among siblings.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

It explains when to use the extracted frame: carry the last frame into generate_clip for longer sequences, then join_clips. It also gives an explicit exclusion ('Not for speech') and notes that get_generation waits for the result.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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