Python Sdk MCP Server

TranscriptFetch Python SDK

Official, typed Python client for the TranscriptFetch API: fetch YouTube transcripts, channels, playlists, and search results as clean, structured data. Sync + async, fully type-hinted.

pip install transcriptfetch-sdk

Quickstart

from transcriptfetch import TranscriptFetch

# api_key falls back to the TRANSCRIPTFETCH_API_KEY env var
tf = TranscriptFetch(api_key="tf_live_...")

t = tf.transcripts.video("https://youtu.be/aircAruvnKk")
print(t.title)
print(t.text)
for seg in t.segments:
    print(f"[{seg.start:.1f}] {seg.text}")

print("credits left:", t.usage.balance)

Get an API key (100 free credits) at https://transcriptfetch.com/app. One credit per successful fetch; failed/blocked/no-transcript requests are free.

Endpoints

tf.transcripts.video(video)                    # single transcript (text + segments)
tf.transcripts.channel(channel, limit=, cursor=)   # a channel's videos (metadata)
tf.transcripts.playlist(playlist, limit=, cursor=) # a playlist's videos
tf.transcripts.search(query, limit=, cursor=)      # search YouTube
tf.transcripts.batch(video_ids)                # up to 50 transcripts in one call
tf.health()                                    # unauthenticated liveness probe

video/channel/playlist accept URLs or raw IDs (normalized automatically).

Pagination

List endpoints are cursor-paginated. Iterate every result without managing cursors:

for video in tf.transcripts.iter_channel("@lexfridman", limit=10):
    print(video.video_id, video.title)

Or page manually via page.next_cursor and the cursor= argument.

Async

import asyncio
from transcriptfetch import AsyncTranscriptFetch

async def main():
    async with AsyncTranscriptFetch() as tf:
        t = await tf.transcripts.video("aircAruvnKk")
        print(t.text)
        async for v in tf.transcripts.iter_search("how transformers work", limit=10):
            print(v.title)

asyncio.run(main())

Errors

All errors subclass TranscriptFetchError. API errors carry .status, .code, .message, and .request_id:

from transcriptfetch import (
    AuthenticationError, InsufficientCreditsError, InvalidRequestError,
    RateLimitError, IdempotencyConflictError, UpstreamUnavailableError,
    InternalServerError, APIError, APIConnectionError, APITimeoutError,
)

try:
    tf.transcripts.video("bad")
except InsufficientCreditsError:
    ...                       # 402: top up at /pricing
except RateLimitError as e:
    print(e.retry_after)      # 429
except APIError as e:
    print(e.status, e.code, e.request_id)

Reliability

• Automatic retries on 429 (honoring Retry-After) and 5xx, with exponential backoff + jitter (max_retries=2 by default).
• Idempotency: every write auto-sends an Idempotency-Key so a retried request is never double-charged. Override per call with idempotency_key=....
• Configurable: TranscriptFetch(api_key=..., base_url=..., timeout=30, max_retries=2). Both clients are context managers and accept a custom http_client= (httpx).

Development

pip install -e ".[dev]"
ruff check . && mypy src && pytest

Tests are fully mocked (no network). MIT licensed.