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YouTube Transcript & Search MCP Server

by com.transcriptout

Submit transcripts batch

submit_transcripts_job

Queue transcripts for MANY videos at once (up to 4000) and get a job_id back immediately — the work continues in the background. Use this instead of calling get_transcript in a loop for more than a handful of videos. Feed it video ids from list_channel_videos or list_playlist_videos (ids_only=true). Next: poll get_transcripts_job until status is 'done', reading finished transcripts from get_transcripts_results as they land. Costs 1 credit per video, charged on submit; duplicates are removed first. Requires a user key (sk_...).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
kindNoTrack kind. Omit to prefer a manual track and fall back to auto
langNoLanguage code for every video, e.g. 'en'. Default 'en'.
formatNo'text' = plain text (default), 'json' = timed segments, 'srt'/'vtt' = subtitle file body, 'srv3' = raw YouTube XML (srv3 does not combine with segment)
videosYesVideo ids or URLs, up to 4000. Duplicates are collapsed.
segmentNoMax characters per segment, for every video in the job. Raise it to 500-1500 when the transcripts are going into embeddings or retrieval. Left out, an auto-generated track is cut into ~180-character segments and a manual one keeps its author's own lines — so pass this when the whole job has to come back at one size.
video_metadataNoInclude each video's title, channel and duration alongside its transcript. Replaces a get_video_info call per video and costs nothing extra.
idempotency_keyNoOptional. Resubmitting the same list with the same key returns the SAME job instead of opening a second one and charging twice.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
countNo
job_idNo
statusNo
creditsNo

TDQS

A4.9/5.0
Behavior5/5

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

The description discloses key non-obvious behaviors: work continues in background, credits are charged on submit, duplicates are removed, and a user key is required. These details go beyond the annotations and inform the agent about side effects and prerequisites.

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 compact, front-loaded with the core behavior, and every sentence earns its place. It includes usage, flow, costs, constraints, and credential requirements without redundancy or unnecessary elaboration.

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 complex asynchronous batch operation, the description covers the input source, output behavior, next steps, pricing, second submit guard, and key requirement. An agent has enough context to select and invoke it correctly without additional reasoning about the workflow.

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?

The schema already covers all 7 parameters, so the baseline is 3. The description adds valuable context for videos, segment behavior, and cost implications, and it explains why to use the batch submit instead of a loop. It slightly exceeds baseline richness.

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?

Description says exactly what it does: queues transcripts for many videos at once, returns a job_id immediately, and continues processing in the background. It clearly distinguishes itself from the synchronous get_transcript tool by emphasizing batch submission and asynchronous results.

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

Usage Guidelines5/5

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

Explicit usage guidance is provided: use instead of get_transcript in a loop, feed IDs from list_channel_videos or list_playlist_videos, and poll get_transcripts_job + get_transcripts_results for results. This gives the agent an unambiguous decision path.

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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TDQS

A4.1/5.0
Disambiguation4/5

Most tools target a distinct resource and action: search, list, metadata, transcript, job lifecycle, and credits. The get_transcripts_job / get_transcripts_result / get_transcripts_results trio is easy to blur at first glance, but the descriptions clearly differentiate job status, one result, and paged results.

Naming Consistency4/5

The set mostly follows a predictable verb_noun pattern: get_, list_, search_, submit_, cancel_. The main deviation is latest_channel_videos, which uses an adjective instead of a verb, and the result/results pair is mildly confusing, but overall naming is coherent and easy to navigate.

Tool Count5/5

14 tools is well within the sweet spot for a server with this scope. Each tool serves a clear niche: video lookup, metadata, single transcripts, batch jobs, and search are all represented without unnecessary or redundant tools.

Completeness5/5

The server provides full coverage of the transcript and search workflow: search YouTube, list channel/playlist videos, fetch or batch-fetch transcripts, check job progress/credits, and read results. There are no obvious missing operations that would force an agent into a dead end.

Resources