YouTube Transcript & Search MCP Server

Batch job results

get_transcripts_results

Read-only

Read finished transcripts from a batch job, in the order submitted. Results appear as they are fetched, so this can be called before the job is done. Each entry is exactly what get_transcript returns for that video, plus its status. Page with next_page_token. Free.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "job_id": "<job_id>"
    },
    "name": "get_transcripts_results"
  }
}

Input Schema

Name	Required	Description
`limit`	No	Entries per page, 1-500 (default 100)
`job_id`	Yes	id returned by submit_transcripts_job
`next_page_token`	No	Token from a previous result

Output Schema

Name	Required	Description	Default
`count`	No
`results`	No
`has_more`	No
`next_page_token`	No

Schema Changelog

Changes observed during successful MCP inspections.

First observedAug 23, 2026

TDQS

A4.3/5.0

Behavior4/5

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

The readOnlyHint=true annotation declares safety, and the description adds meaningful behavior: partial results appear before job completion, each entry includes status, pagination via next_page_token, and it's free. No contradiction; the extra context complements the annotations.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Three sentences with high information density and no fluff. It front-loads the core purpose and then adds caveats and pagination. It could be slightly more structured or clearer about parameter usage, but it earns a solid score.

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?

With an output schema present, the description doesn't need to explain return values in depth. It covers key operational details: partial results before completion, per-entry format, pagination. Minor lack of explicit rate limits or error conditions, but not essential given the output schema and annotations.

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 the description doesn't need to document parameters in depth. It still adds value by explaining that order is submission order and confirming pagination behavior through next_page_token—semantics beyond field names.

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 clearly states the tool reads finished transcripts from a batch job in submission order, which distinguishes it from siblings like get_transcripts_job (job metadata) and get_transcript (single transcript). The verb 'read' plus the resource 'finished transcripts from a batch job' makes the purpose unambiguous.

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 says this can be called before the job is done since results appear as fetched—useful timing guidance. It also notes each entry equals what get_transcript returns, indirectly distinguishing it from that sibling. It lacks explicit when-not-to-use conditions, but the context is mostly clear.

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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