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ztemerbekov

A1 Yandex KIT MCP

Upload video

upload_video

Upload a product video (MP4, MOV, WebM, AVI, FLV; up to 100 MB) and get a video ID. Poll the video status until READY, then attach it to a variant via create_variant or update_variant.

Instructions

Upload a product video via multipart/form-data and queue it for processing. Max size 100 MB; formats mp4, mov, webm, avi, flv. Videos are deduplicated by content: uploading identical bytes returns the existing video. The response carries the video ID — poll it with get_video until the status is READY, then attach the video to a variant through media in create_variant / update_variant. Provide exactly one source: file_path or content_base64.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
filenameNoFile name to send; it becomes the video title. Required with content_base64; defaults to the basename of file_path.
file_pathNoAbsolute path to the video file on the local machine running this MCP server. Mutually exclusive with content_base64.
content_base64NoVideo content as a base64 string. Mutually exclusive with file_path. Prefer file_path for large videos.
Behavior5/5

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

No annotations are provided, so the description carries full behavioral burden. It discloses deduplication behavior, max size, accepted formats, return value (video ID), and the need to poll for status — all beyond what the schema reveals.

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 concise and well-structured: main action first, then constraints, then post-upload workflow. Every sentence adds value with no fluff.

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?

For a tool with no output schema and zero annotations, the description provides all necessary context: how to invoke, constraints, return value, and subsequent steps. It is complete for an AI agent to use correctly.

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 coverage is 100%, so baseline is 3. The description adds useful semantics: filename becomes the video title, defaults to basename of file_path, and recommends file_path over content_base64 for large videos, enhancing schema meaning.

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 the tool's specific purpose: uploading a product video via multipart/form-data and queuing it for processing. It distinguishes this from sibling tools like upload_file by specifying 'product video' and outlining the processing lifecycle.

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

Usage Guidelines4/5

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

Provides clear usage context: videos are deduplicated, uploading identical bytes returns the existing video, and the caller should poll get_video until READY then attach via create_variant/update_variant. It does not explicitly name alternative tools to avoid, but the workflow guidance is strong.

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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