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YouTube Audio & Transcript MCP Server

Check a conversion or transcript job

get_job
Read-onlyIdempotent

Poll a pending YouTube audio or transcript job to retrieve its result. Returns a fresh download link or transcript; failed jobs return the API error.

Instructions

Check the status of a job returned by youtube_to_audio or youtube_transcript when it did not finish in time. Long-polls up to waitSeconds (max 25). When the job is done: for api='audio' returns a fresh short-lived download link; for api='transcript' returns the transcript content. Failed jobs return the API error code and message.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
apiNoWhich API created the job. Default: audio
jobIdYesThe jobId returned by youtube_to_audio or youtube_transcript
maxCharsNoTranscript jobs only: truncate the returned transcript. Default: 100000
waitSecondsNoLong-poll up to this many seconds for completion. Default: 25

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A4.5/5.0
Behavior5/5

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

Annotations already establish read-only, idempotent, non-destructive semantics, but the description adds real behavioral detail beyond them: long-polling up to waitSeconds (max 25), api-dependent return shapes (a fresh short-lived download link vs. transcript content), and the failure path (API error code and message). That is substantially more than the structured fields convey.

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 sentences, front-loaded with the identifying purpose and condition, then the polling contract, then the return/error semantics. Every sentence carries distinct information with no repetition.

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?

No output schema exists, and the description compensates by describing both success return values per api and the failure return values. Combined with 100% schema coverage, an agent has everything needed to call this correctly.

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 description coverage is 100%, so the schema already documents all four parameters including the waitSeconds maximum and the api enum. The description restates the waitSeconds cap and alludes to the api switch but adds no syntax or format detail beyond the schema — baseline 3 is appropriate.

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?

Specific verb+resource ('Check the status of a job') plus the exact provenance of the resource ('returned by youtube_to_audio or youtube_transcript'), which cleanly separates it from both siblings. An agent can tell this is a polling/status tool, not a job-creation tool, without opening any schema.

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?

Gives a clear trigger condition: use it when a job 'did not finish in time' from one of the named sibling tools. It does not state any exclusions or failure fallbacks (e.g. what to do if the job is still pending after WaitSeconds), so it stops short of full when/when-not routing.

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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