Skip to main content
Glama

videoCutRandom

Extract a video clip from any video URL by specifying start and end positions as percentages of duration. Provides the cut segment directly.

Instructions

输入视频链接,输入截取的起始和结束位置,即可截取位置获得对应的视频片段

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
endPlaceNo裁剪视频的结束位置,时长的百分比
videoUrlYes视频URL地址
startPlaceNo裁剪视频的起始位置,时长的百分比

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. First observedv0.1.16

TDQS

B3.2/5.0
Behavior2/5

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It only restates the basic cut behavior and does not explain output format, delivery mechanism, failure modes, or why the tool name includes 'Random'. This leaves important behavioral unknowns.

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 a single, concise sentence with no filler and communicates the core input and output relationship. It is appropriately short, though it does not add secondary structure like numbered steps or result expectations.

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?

For a simple three-parameter tool with a complete schema, the description is minimally adequate, but there is no output schema and no annotations. It does not explain what the returned video clip looks like, how it is delivered, whether processing is asynchronous, or what the 'Random' in the tool name means.

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?

Schema description coverage is 100%, and the schema already documents videoUrl, startPlace, and endPlace, including percentage semantics. The description adds no new parameter meaning beyond saying start and end positions are involved, so the baseline of 3 applies.

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

Purpose4/5

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

The description clearly states a specific action—cut a video segment using a URL plus start and end positions. However, it does not distinguish this from sibling video2video, and the 'Random' part of the tool name is never explained, leaving some ambiguity about scope.

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 description implies the tool is for cutting a video based on user-supplied positions, which gives basic usage context. It does not explicitly state when to choose this tool over siblings like video2video, nor are exclusions or prerequisites mentioned.

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

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/Runninghcm/MathMind-MCP-SERVER'

If you have feedback or need assistance with the MCP directory API, please join our Discord server