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Python
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video_stabilize

Stabilize shaky video using motion vector analysis, with adjustable smoothing and optional zoom to avoid black borders.

Instructions

Stabilize a shaky video using motion vector analysis.

Args: input_path: Absolute path to the input video. smoothing: Smoothing strength (default 15, higher = more stable). zooming: Zoom percentage to avoid black borders (default 0). output_path: Where to save the output. Auto-generated if omitted.

Input Schema

NameRequiredDescriptionDefault
zoomingNo
overwriteNoExplicitly allow replacing existing media. Existing non-media files are protected.
smoothingNo
input_pathYes
output_pathNo

Output Schema

NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed1 schema field changedv1.16.4
    • addedInput schema / properties / overwrite
      Added value: +{
+  "default": false,
+  "description": "Explicitly allow replacing existing media. Existing non-media files are protected.",
+  "title": "Overwrite",
+  "type": "boolean"
+}
  2. Changed1 schema field changedv1.16.0
    • addedInput schema / additionalProperties
      Added value: +false
  3. Addedv1.5.1

TDQS

A3.8/5.0
Behavior3/5

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

No annotations are provided, so the description carries the full behavioral burden. It discloses the motion-vector method, smoothing effect, zooming purpose, and auto-generated output path, but does not explain overwrite behavior, whether the input is modified, permissions, limits, or processing side effects.

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

Conciseness4/5

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

The description is front-loaded with the core action and method, then uses a compact Args section. It is appropriately sized, though the argument list partly duplicates schema names and could be tightened.

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

Completeness3/5

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. However, for a no-annotation video mutation tool, the description omits operational constraints such as supported formats, overwrite safety, and whether audio/metadata are preserved.

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

Parameters4/5

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

Schema description coverage is only 20%, but the description compensates by explaining four of five parameters, including non-obvious semantics for smoothing ("higher = more stable"), zooming ("avoid black borders"), and output_path auto-generation. The remaining overwrite parameter is documented in the schema itself.

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?

States a specific verb and resource ("Stabilize a shaky video") and names the technique ("motion vector analysis"). This is clearly distinguishable from sibling video-processing tools by its unique stabilization purpose.

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

Usage Guidelines3/5

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

The phrase "shaky video" implies the use case, but the description gives no explicit when-to-use, when-not-to-use, or alternative-tool guidance. It does not mention related tools such as rescue planning or quality inspection.

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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