粘贴 YouTube URL，即可获取字幕。纯文本，无需注册。
在任何 YouTube 页面上一键提取字幕。支持多种输出格式（RAG、JSON、Markdown）。
将 rippr 连接到 Claude、Cursor 或任何兼容 MCP 的客户端。将每个字幕保存到 ~/rippr/transcripts/ 并将文件路径返回给模型。
仅限桌面客户端。 rippr 作为本地 stdio 进程运行，因此它适用于 Claude Desktop、Claude Code CLI 和 Cursor。它不适用于云托管客户端（网页版 claude.ai、Claude 移动应用或手机/网页版 Claude Code），因为这些客户端无法启动本地进程。
npx rippr-mcp添加到 Claude Desktop (~/Library/Application Support/Claude/claude_desktop_config.json)：
{
"mcpServers": {
"rippr": {
"command": "npx",
"args": ["-y", "rippr-mcp"]
}
}
}然后询问："Rip this YouTube video: [url]"。查看 mcp/README.md 获取完整的工具接口说明。
Related MCP server: YouTube Transcript MCP
RAG (.txt) — 单个连续文本块，针对分块和嵌入进行了优化
结构化 (.json) — 带有元数据的时间戳片段
可读 (.md) — 带有标题和格式的 Markdown
多策略提取以实现最大可靠性：
Innertube API — YouTube 的内部播放器 API（Android 客户端）
HTML 抓取 — 从页面源代码解析 ytInitialPlayerResponse
字幕面板 — 作为最后手段打开 YouTube 内置的字幕面板
以多种格式（srv3、timedtext、JSON3）解析字幕 XML。在出现瞬时故障时使用指数退避重试。
完全在您的机器上运行。没有数据发送到外部服务器。没有账户，没有跟踪。仅与 YouTube 自身的 API 通信。
Rippr 是一个非官方的社区构建工具。它不隶属于 YouTube 或 Google LLC，也不受其认可或赞助。YouTube 是 Google LLC 的商标。
Rippr 通过 YouTube 自身应用使用的端点访问公开可用的 YouTube 字幕数据。使用受 YouTube 服务条款约束，使用风险自负。作者对封禁、速率限制、账户操作或任何其他使用后果不承担任何责任。
如果 YouTube 以导致提取失败的方式更改其内部 API，该工具可能会在不另行通知的情况下停止工作。对于长期生产使用，请考虑使用带有 API 密钥的官方 YouTube Data API v3（此软件包目前不支持）。
Extract the full transcript from a YouTube video. By default, saves the transcript as a Markdown file to disk and returns a resource_link + metadata (title, channel, language, duration, word count, saved path, preview). The full transcript text is NOT returned by default — this keeps context lean and gives the user a persistent file they can reuse. After calling, always tell the user where the file was saved. If you need the transcript text later (summarize, search, extract quotes), read the returned resource rather than re-ripping. Pass return_text: true only for short clips or when the user explicitly asks for the transcript inline. Pass format: 'segments' to save timestamped JSON instead of Markdown.
| Name | Required | Description | Default |
|---|---|---|---|
| url | Yes | YouTube video URL. Supports any YouTube URL format (watch, youtu.be, embed, shorts, or bare 11-char ID). | |
| format | No | Saved file format. 'text' writes a Markdown file with YAML frontmatter and a continuous transcript block (best for RAG/LLM). 'segments' writes a JSON file with timestamped segments (best for chapter markers, precise citations). Default: text. | |
| save_path | No | Optional override for where to save the transcript. Can be an absolute path, a ~/-relative path, a directory (file is named automatically), or a full file path. When you save outside the default directory, the returned resource_link still points to the saved file so it can be read later. Default: ~/rippr/transcripts/<slug>_<videoId>.<ext> | |
| return_text | No | If true, include the full transcript text in the tool response alongside the resource_link. Default: false. Use true only for short clips or when the user explicitly wants the transcript inline. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Despite no annotations, the description fully discloses default behavior (saves file, does not return text), rationale (keep context lean, persistent file), and side effects (saves to disk). Could explicitly mention idempotency or rate limits, but overall strong.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Seven sentences, all contributing meaningful information. Front-loaded with purpose and key behavioral notes. No redundant or unnecessary content.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Covers all critical aspects: default behavior, parameter usage, post-call action (tell user where saved). No output schema, but description is sufficient for agent to correctly invoke and follow up. Minor missing detail on resource_link format, but not essential.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100%, but description adds significant value: explains default behavior for format and return_text, supported URL formats, and save_path resolution details. Provides usage recommendations that go beyond schema descriptions.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
Description states 'Extract the full transcript from a YouTube video' with a specific verb and resource, clearly defining the tool's function. No sibling tools exist, so differentiation is not an issue.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
Provides explicit guidance: default saves file and returns metadata, advises against re-ripping by reading resource, specifies when to use return_text (short clips or explicit request) and format alternatives. Covers context and exclusions thoroughly.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
With only one tool, there is no possibility of confusion. The single tool 'rip_transcript' has a clear and distinct purpose.
The single tool name 'rip_transcript' follows a consistent verb_noun pattern. No naming conflicts exist.
The server has only one tool, which is minimal but acceptable given its focused purpose of ripping YouTube transcripts. While slightly under-scoped, it provides a complete interface for its intended function.
The tool covers the core task of transcript extraction with options for format and inline text. However, it lacks complementary tools such as listing videos or batch processing, leaving minor gaps.
Unclaimed servers have limited discoverability.
If you are the server author, to access and configure the admin panel.
YouTube transcripts, search, channel/playlist listings and upload tracking for AI agents. No signup.
Fetch the full transcript of any YouTube video as clean text. No API key, no signup.
Clean YouTube transcripts for agents: single videos, channels, playlists, plus AI caption cleanup.
Any video URL to LLM-ready transcript. ASR built in, no captions needed. TikTok, X, TED and more.
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