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Brief for writing metadata

generate_metadata
Read-only

Gather transcripts with timestamps, templates, and YouTube rules to prepare titles, descriptions, chapters, and tags for a video or Short, then verify the results before publishing.

Instructions

Collect everything needed to write a strong title, description, chapters and tags: the transcript with timestamps (from text you pass, a .srt/.vtt/.txt file, an uploaded video’s captions, or by transcribing a local file), the chosen template and YouTube’s rules. You then write the metadata and check it with preview_metadata.

Input Schema

NameRequiredDescriptionDefault
notesNoAnything else: audience, links to include, tone.
formatNovideo
channelNoWhich connected channel: channel ID (UC...), @handle or name. Omit to use the default channel.
languageNoLanguage to write the metadata in.
templateNo
video_idNoUse the captions of this video on your channel.
file_pathNoTranscribe this local video first (needs transcription configured).
transcriptNoTranscript or notes as plain text.
transcript_pathNo.srt, .vtt or .txt file on this computer.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.0.1

TDQS

A4.2/5.0
Behavior4/5

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

The annotations already declare readOnlyHint=true and openWorldHint=true, and the description adds that this tool gathers inputs rather than writing final metadata. It also lists the transcript sources, though it does not describe return shape, auth needs, or rate limits.

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 sentences, front-loaded with the core purpose and then the next step. The parenthetical listing transcript sources is dense but useful, with no wasted wording.

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 read-only gathering tool with nine optional parameters and no output schema, the description covers the main inputs and the follow-up step. It could say more about the returned brief, but the schema and annotations carry most remaining detail.

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 78%, so the schema already documents most parameters. The description maps transcript sources to transcript, transcript_path, video_id, and file_path, and mentions the template, but adds little for notes, channel, language, or format beyond what the schema provides.

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 uses a specific verb, "Collect," and names the concrete resource: everything needed to write a title, description, chapters, and tags. It also distinguishes this gathering step from preview_metadata, so an agent can tell it is not the final metadata writer.

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?

It clearly positions the tool before writing metadata and explicitly says to check the result with preview_metadata. It does not spell out when not to use it or compare against every sibling, but the workflow context is clear.

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