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Update video details

update_video

Change a YouTube video's title, description, tags, or privacy status. Only the fields you pass change; the rest are preserved.

Instructions

Change a video's title, description, tags or privacy. Only the fields you pass change: the rest are read back and preserved first, because the API replaces the whole snippet and would otherwise blank them. Reversible, so it is not confirm-gated.

Input Schema

NameRequiredDescriptionDefault
tagsNo
titleNo
accountNoWhich connected channel to act on (name or @handle). Required when more than one is connected: without it the call fails and lists the choices rather than picking one.
video_idYes
descriptionNo
privacy_statusNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed2 schema fields changedv3.0.2
    • removedInput schema / $schema
      Removed value: -"http://json-schema.org/draft-07/schema#"
    • removedInput schema / additionalProperties
      Removed value: -false
  2. First observedv1.0.0

TDQS

A4.2/5.0
Behavior5/5

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

Goes well beyond the annotations: it explains partial-update semantics and the underlying reason (the API replaces the whole snippet, so untouched fields are read back and preserved first), and it explains why it is not confirm-gated (reversible). This is exactly the kind of side-effect disclosure a mutation tool needs. The 'not confirm-gated' framing is consistent with destructiveHint=false.

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?

Two tight sentences with zero filler; the field list is front-loaded and the preservation/confirm-gating rationale follows in support of it. Every clause earns its place.

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?

For a mutation tool with no output schema, the description covers what changes, what is preserved, and the safety posture, which is the critical information. It omits any mention of failure modes or the return value, but those are minor given the annotations and schema carry the rest.

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 coverage is only 17%, so the description must compensate. It names four of the six parameters (title, description, tags, privacy) and states that only passed fields are changed, which adds meaningful semantics; the account parameter's fallback behavior is documented in its own schema entry and video_id is implicit. Coverage of types/formats remains thin, so a baseline 3 fits.

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 ('Change') and resource ('a video's title, description, tags or privacy'), enumerating the mutable fields. It is immediately distinguishable from get_video, delete_video, and search_videos among the siblings.

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 context for use is implied by naming editable fields and by the note that it is reversible and not confirm-gated, which tells the agent it can call it without extra confirmation. However, no alternative tool is named and no when-not condition is given.

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