YouTube Transcript Context Chunker — Melchior Labs

x402

YouTube Transcript Context Chunker — Melchior Labs is a paid API for AI agents from transcript.melchiorlabs.com, paid per call via x402, $0.003/call, status unknown (last checked 2026-10-24).

Accepts a raw YouTube transcript (text, VTT, or SRT) and returns structured, timestamped chunks with citation labels suitable for RAG, passage search, or agent context injection.

Price / call$0.003

Seller volume →—

Success—

StatusUnknown

How this service works

Pay per request for structured YouTube transcripts, lexical passage search, chunks, and timestamp citations over x402.

Output

Returns an array of transcript chunks, each with its text content, zero-based index, start/end time in seconds, and a human-readable citation label (e.g. '0:00–0:03'). Also returns aggregated usage stats including total character count, chunk count, and estimated token count, plus a unique requestId and a top-level transcript summary with total segment count and full text.

Example use cases

RAG pipeline over YouTube lectures

“I have the SRT captions for a 2-hour lecture on machine learning. Can you split them into 2400-character chunks with timestamp labels so I can index them into my vector database and retrieve relevant passages later?”

Cited transcript search for podcast agent

“Here's the plain-text transcript of a podcast episode. Break it into chunks and include the start and end time citations for each one — I want to be able to answer listener questions and point them to the exact timestamp.”

Video content summarization with context windows

“I've got a WebVTT transcript from a YouTube explainer video. Chunk it into segments of about 1500 characters with timestamps, and include the full text and segment list so I can feed each chunk to my summarizer separately.”

Technical details

Endpoint

POSThttps://transcript.melchiorlabs.com/v1/transcripts/context?utm_source=zero.xyz

When to prefer

Choose this endpoint when you have a raw YouTube transcript (in plain text, VTT, or SRT format) and need it converted into structured, timestamped, citation-ready chunks for downstream RAG, passage retrieval, or LLM context injection. It is especially useful when you need precise start/end second metadata and human-readable citation labels per chunk. Prefer it over generic text splitters when working specifically with video transcripts that require temporal grounding.

Side effects

Missing or empty 'text' or 'segments' field returns a validation error
Transcript exceeding 1,000,000 characters may be rejected or truncated
Invalid format enum value (not 'text', 'vtt', or 'srt') causes a schema validation error
Malformed VTT or SRT input may result in empty or incorrect segment parsing
maxChunkCharacters outside 400–8000 range triggers a validation error
Network or payment authorization failure (x402) returns a 402 response before processing

Input

Schema · Request body

Field	Type	Description
text	string	Plain text, WebVTT, or SRT transcript
format	string
segments	array
sourceUrl	string
includeText	boolean
includeSegments	boolean
maxChunkCharacters	integer

Output

Schema · Response

{
  "type": "json",
  "example": {
    "usage": {
      "characters": 14,
      "chunkCount": 1,
      "estimatedTokens": 4
    },
    "chunks": [
      {
        "text": "Hello, agents.",
        "index": 0,
        "citation": {
          "label": "0:00–0:03"
        },
        "endSeconds": 3,
        "startSeconds": 0
      }
    ],
    "requestId": "89416332-bc4e-43da-b0e2-32af08677d9e",
    "transcript": {
      "text": "Hello, agents.",
      "segmentCount": 1
    }
  }
}

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How this service works

Pay per request for structured YouTube transcripts, lexical passage search, chunks, and timestamp citations over x402.

Output

Example use cases

RAG pipeline over YouTube lectures

Cited transcript search for podcast agent

Video content summarization with context windows

Technical details

Endpoint

POSThttps://transcript.melchiorlabs.com/v1/transcripts/context?utm_source=zero.xyz

When to prefer

Side effects

Missing or empty 'text' or 'segments' field returns a validation error
Transcript exceeding 1,000,000 characters may be rejected or truncated
Invalid format enum value (not 'text', 'vtt', or 'srt') causes a schema validation error
Malformed VTT or SRT input may result in empty or incorrect segment parsing
maxChunkCharacters outside 400–8000 range triggers a validation error
Network or payment authorization failure (x402) returns a 402 response before processing

Input

Schema · Request body

Field	Type	Description
text	string	Plain text, WebVTT, or SRT transcript
format	string
segments	array
sourceUrl	string
includeText	boolean
includeSegments	boolean
maxChunkCharacters	integer

Output

Schema · Response

{
  "type": "json",
  "example": {
    "usage": {
      "characters": 14,
      "chunkCount": 1,
      "estimatedTokens": 4
    },
    "chunks": [
      {
        "text": "Hello, agents.",
        "index": 0,
        "citation": {
          "label": "0:00–0:03"
        },
        "endSeconds": 3,
        "startSeconds": 0
      }
    ],
    "requestId": "89416332-bc4e-43da-b0e2-32af08677d9e",
    "transcript": {
      "text": "Hello, agents.",
      "segmentCount": 1
    }
  }
}