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aivideocheck

AI Video Check MCP Server

by aivideocheck

Check a video file

check_file

Analyze a local video file to determine if it was generated by AI. Returns a probability score for picture and sound.

Instructions

Check whether a video file on this computer was generated by AI. Uploading the original file gives a more reliable result than a link. Spends credits: one per started minute of video.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
pathYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.0.0

TDQS

A3.6/5.0
Behavior4/5

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

With annotations present (readOnlyHint=false, openWorldHint=true, idempotentHint=false, destructiveHint=false), the bar is lower, and the description adds real value: the credit cost ('one per started minute of video') and the reliability tradeoff between file and link. It does not mention supported formats or upload limits, but the cost disclosure is meaningful behavior not captured anywhere else.

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?

Three short sentences, front-loaded with the core purpose, then the input-quality hint, then the cost. No filler or redundancy.

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

Completeness4/5

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 needn't be described. The description covers purpose, input preference, and cost — enough for an agent to select and invoke the tool — though format/size constraints on the local file are left unstated.

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

Parameters2/5

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

Schema description coverage is 0% and the single required 'path' parameter has no documented semantics. The phrase 'a video file on this computer' hints that path is a local filesystem path, but there is no format, extension, or size guidance, so the description only partially compensates for the empty schema.

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?

States a specific verb and resource: check whether a video file on this computer was AI-generated. It implicitly contrasts with check_video by noting that uploading the original file is more reliable than a link, but it never names the sibling, so differentiation is inferential rather than explicit.

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?

It guides input choice ('uploading the original file gives a more reliable result than a link') but never says when to call check_file versus check_video, get_check, or get_balance, nor any prerequisite conditions. Usage is implied by the input-type contrast rather than stated.

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

Deploy Server

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