YouTube Video Summarizer

YouTube

Operated by Sourcelane✓ verified

A YouTube-focused video summarization connector that ingests video transcripts (manual or auto-generated) and uses transcript parsing plus large language model summarization to produce structured textual artifacts and metadata: concise-to-long summaries written with specific details (names, numbers, tools, conclusions), 5–8 actionable key takeaways, topic tags, timestamped chapter markers with one-line section summaries that can follow or correct creator chapter breaks, verbatim timestamped quotes, optional long-form blog post drafts, and optional plain-text transcripts; the connector also extracts video metadata such as identifiers, title, channel info, upload date, duration and view counts, supports multilingual output/translation of summaries and non-English transcripts, and is designed to handle long recordings (podcasts, lectures, tutorials) by processing large transcripts and generating per-video structured outputs using an LLM-based pipeline.

Verified Sep 28, 12:11 AM

$0.06

per video

Up to 16 per call. Only pay for results returned; failed calls are refunded.

What people use it for

Research and learning: digest lectures, conference talks and interviews quickly
Content repurposing: convert videos into blog posts, newsletters and social threads
Chapter generation: create or correct timestamped chapters for video descriptions
Competitive monitoring: summarise new videos from rival channels on a schedule
Knowledge bases: index a channel’s back catalogue as searchable text
Meeting and webinar notes: summarise recorded sessions published to YouTube

Trust & reliability

7d uptime trend

Window	Uptime	Success rate	p50	p95
24h	—	—	—	—
7d	—	—	—	—
30d	—	—	—	—

Calling contract

Call it through Sourcelane's gateway or MCP server. We run the connector, apply your agent's spend guardrails, and bill only the results returned.

Call it via Sourcelane

curl -X POST https://cool-puffin-608.convex.site/v1/call \
  -H "Authorization: Bearer sl_live_your_agent_key" \
  -H "Content-Type: application/json" \
  -d '{"listing":"youtube-video-summarizer","params":{"videoUrls":["https://www.youtube.com/watch?v=8jPQjjsBbIc"]},"maxResults":10}'

Request params

Field	Type	Required	Description
`videoUrls`	array	required	YouTube video URLs or IDs, one per line. Up to 50 per run.
`summaryLength`	string	optional	How detailed the main summary should be. Key takeaways, chapters and quotes are produced regardless.
`includeChapters`	boolean	optional	Produce timestamped chapters with a title and one-line summary each, ready to paste into a video description.
`includeKeyQuotes`	boolean	optional	Pull 3-6 verbatim, timestamped quotes worth sharing or citing.
`includeBlogPost`	boolean	optional	Also generate a 600-1000 word Markdown blog post based on the video. Slower and uses more model output.
`includeTranscript`	boolean	optional	Add the plain-text transcript to each record.
`outputLanguage`	string	optional	Language to write the summary in, e.g. English, Spanish, German. Leave empty to match the transcript language.
`languages`	array	optional	Caption languages to try, in order of preference (e.g. en, es, de). Manual captions are preferred over auto-generated ones.
`model`	string	optional	Language model used to write the summary.

Each result contains

Field	Type	Description
`inputUrl`	string	Input Url
`videoId`	string	Video Id
`url`	string	Url
`title`	string	Title
`channelTitle`	string	Channel Title
`channelId`	string	Channel Id
`uploadDate`	string	Upload Date
`durationSeconds`	integer	Duration Seconds
`viewCount`	integer	View Count
`transcriptLanguage`	string	Transcript Language
`transcriptIsAutoGenerated`	boolean	Transcript Is Auto Generated
`transcriptWordCount`	integer	Transcript Word Count
`transcriptTruncated`	boolean	—
`transcript`	null	—
`summary`	string	—
`keyTakeaways`	array	—
`topics`	array	—
`chapters`	array	—
`keyQuotes`	array	—
`blogPost`	null	—
`model`	string	—
`promptTokens`	integer	—
`completionTokens`	integer	—
`proxyUsed`	string	—
`status`	string	—
`error`	null	—

Use YouTube Video Summarizer from Claude, ChatGPT or Cursor

Pick your tool and connect in under a minute. Then just ask — for example: “Get the transcript of this video and turn it into a blog outline: <url>”

Connect Claude

Web, desktop and mobile. Paste one URL.

1Copy your personal connector URL
2In Claude open Settings → Connectors → Add custom connector
3Paste the URL and click Add — done

Manual setup (config files, REST, Python) +

Claude Code

Adds the Sourcelane MCP server with your key as a header.

terminal

claude mcp add --transport http sourcelane https://cool-puffin-608.convex.site/mcp \
  --header "Authorization: Bearer sl_live_your_agent_key"

Claude Desktop (config file)

Alternative to the connector URL: add to claude_desktop_config.json, then restart Claude.

claude_desktop_config.json

{
  "mcpServers": {
    "sourcelane": {
      "command": "npx",
      "args": [
        "-y",
        "mcp-remote",
        "https://cool-puffin-608.convex.site/mcp",
        "--header",
        "Authorization:${AUTH_HEADER}"
      ],
      "env": {
        "AUTH_HEADER": "Bearer sl_live_your_agent_key"
      }
    }
  }
}

Cursor & Windsurf

Add to ~/.cursor/mcp.json (or Windsurf's mcp_config.json).

mcp.json

{
  "mcpServers": {
    "sourcelane": {
      "url": "https://cool-puffin-608.convex.site/mcp",
      "headers": {
        "Authorization": "Bearer sl_live_your_agent_key"
      }
    }
  }
}

VS Code

Save as .vscode/mcp.json. VS Code prompts for your key once.

.vscode/mcp.json

{
  "servers": {
    "sourcelane": {
      "type": "http",
      "url": "https://cool-puffin-608.convex.site/mcp",
      "headers": {
        "Authorization": "Bearer ${input:sourcelane-key}"
      }
    }
  },
  "inputs": [
    {
      "type": "promptString",
      "id": "sourcelane-key",
      "description": "Sourcelane agent key",
      "password": true
    }
  ]
}

ChatGPT Custom GPT (Actions)

Create a GPT → Actions → Import from URL, then Authentication: API Key, Bearer.

OpenAPI schema URL

https://sourcelane-amber.vercel.app/openapi.json

REST

One POST. Pass maxResults to cap cost.

curl

curl -X POST https://cool-puffin-608.convex.site/v1/call \
  -H "Authorization: Bearer sl_live_your_agent_key" \
  -H "Content-Type: application/json" \
  -d '{"listing":"youtube-video-summarizer","params":{"videoUrls":["https://www.youtube.com/watch?v=8jPQjjsBbIc"]},"maxResults":10}'

Python, LangChain, CrewAI, OpenAI Agents SDK…

Wrap the REST call as a tool, or point an MCP client at the endpoint.

python

import requests

res = requests.post(
    "https://cool-puffin-608.convex.site/v1/call",
    headers={"Authorization": "Bearer sl_live_your_agent_key"},
    json={
        "listing": "youtube-video-summarizer",
        "params": {"videoUrls":["https://www.youtube.com/watch?v=8jPQjjsBbIc"]},
        "maxResults": 10,
    },
    timeout=300,
)
body = res.json()
print(body["receipt"]["chargedMicros"], "micro-USD for", body["receipt"]["results"], "results")
print(body["data"])

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