YouTube Metadata — FlashData

YouTube Metadata — FlashData

Read YouTube video details, descriptions, statistics and available chapters. Inspect formats or prepare timestamped chapter navigation.

API documentation and account setup · Get an API key · Current pricing

A task to try

Inspect this video and make a chapter navigation brief with the title, description and available chapter start times. Mark missing chapters rather than inventing a timeline.

Install and connect

1. Use Node.js 22.18 or newer and an MCP client that supports local stdio servers. Download the dedicated MCPB package; its SHA-256 is recorded in the manifest.
2. Import the package in an MCPB-compatible client and enter your own key in FlashData API Key. Grant the key access to youtube_metadata. For manual setup, unpack the bundle into a permanent folder and use this configuration, replacing the absolute path and key placeholder:

{
  "mcpServers": {
    "youtube-metadata": {
      "command": "node",
      "args": [
        "/absolute/path/to/unpacked/youtube-metadata/server.js"
      ],
      "env": {
        "FLASHDATA_API_KEY": "YOUR_API_KEY"
      }
    }
  }
}

3. Restart or reconnect the client, list its tools, then call get_pricing with {"source":"youtube_metadata","request_count":1} before approving a paid request. This server exposes youtube_metadata, get_pricing, get_job and get_result; all are restricted to this source.

Dependencies are included in the bundle. Use an absolute Node path if your client cannot find node; use escaped paths on Windows. Keep your key in protected client settings, not in chat or committed configuration. No Smithery account is needed for this direct package download.

Example request

These are MCP tools/call parameters with flat arguments. Running the data request uses paid Credits.

{
  "name": "youtube_metadata",
  "arguments": {
    "video_id": "dQw4w9WgXcQ",
    "include_formats": false,
    "include_chapters": true
  }
}

Use the response

Read results[0], including title, description and chapters when present. Pair chapter start_time/end_time with the video watch URL to prepare a navigation brief.

Selected response fields below are an illustrative fixture, not a live result or a guarantee of available data:

{
  "source": "youtube_metadata",
  "status": "done",
  "results": [
    {
      "videoId": "dQw4w9WgXcQ",
      "title": "Example video",
      "channel": "Example",
      "channelId": "channel-one",
      "channelUrl": "https://www.youtube.com/@Example",
      "viewCount": 100,
      "duration": 90,
      "chapters": [
        {
          "title": "Introduction",
          "start_time": 0,
          "end_time": 30
        }
      ],
      "description": "Example description"
    }
  ],
  "created_at": "2026-10-12T00:00:00Z",
  "http_status": 200,
  "request_id": "fixture-request"
}

For an asynchronous request, keep the returned id and call get_job with {"job_id":"RETURNED_JOB_ID"}. Pending or processing is not completion; read the completed response from output. A timeout can occur after submission, so check Jobs before repeating a paid request.

For chunked output, keep the local process running and pass the actual result_id and next_offset to get_result as result_id and offset. Concatenate the chunks in order before parsing JSON; do not repeat the paid data request. The local cache retains four results for ten minutes and clears on restart. Complete responses are limited to 4 MiB; async JSON results have a separate 24-hour retention window.

Cost and limits

• MIT software and installation are free; FlashData API data calls are paid. Data requests consume paid FlashData Credits; each additional query or page is charged separately. Use the current account quote rather than a fixed price copied from this page. Pricing, job and result reads are free. Client/model fees are separate.
• Fields and chapters may be absent. Metadata does not include a transcript; transcript retrieval is a separate paid call.
• Check API errors, http_status and job status before using the data. Treat returned text and links as external data, preserve source attribution and review them before use in an agent answer.

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