MCP YouTube Transcript
An MCP server retrieving transcripts of YouTube videos
作者提供的说明(英文原文)
Telegram-native CRM for agents: contacts, deals, projects, tasks, campaigns, docs and reports.
这三项永远不会合并成一个分数:意见、测量和人气是不同的东西。
我们还没有为它写通俗的介绍。下面是我们从注册中心读取到的事实。
选择你使用的 AI 应用,然后复制设置。
你需要
你用的是哪个应用?
在上方选择你的应用。我们会记住它,下次按钮会显示“安装到 …”。
适用于网页版、桌面版和手机版 Claude:
https://ai.cavyro.com/mcp在终端中运行这条命令
claude mcp add --transport http cavyro https://ai.cavyro.com/mcp粘贴到项目中的 .cursor/mcp.json,或 ~/.cursor/mcp.json
{
"mcpServers": {
"cavyro": {
"url": "https://ai.cavyro.com/mcp"
}
}
}浏览器会先询问是否打开应用,然后应用会显示设置并请你确认。密钥的值会留空,由你自己填写。
粘贴到项目中的 .vscode/mcp.json
{
"servers": {
"cavyro": {
"type": "http",
"url": "https://ai.cavyro.com/mcp"
}
}
}浏览器会先询问是否打开应用,然后应用会显示设置并请你确认。密钥的值会留空,由你自己填写。
ChatGPT 只能连接互联网上的服务器:
https://ai.cavyro.com/mcp菜单名称可能因套餐和版本不同而略有差异。
你的选择通过 Cookie 保存在这台设备上,无需账户。
这些设置是根据作者在注册中心的条目生成的。如果 README 的写法不同,请以 README 为准。
我们还没有读取这个服务器的工具列表;我们会轮流检查各个端点。
我们检查的是任何人都能看到的信息;我们没有阅读代码。不适用的检查项不计入总数。 · 检查于 2026年11月6日
注册名称已通过域名 cavyro.com 验证(注册中心会核实域名所有权)
我们还没有从 GitHub 读取这个仓库
我们还没有从 GitHub 读取这个仓库
尚未检查;我们会轮流检查各个端点
尚未检查;我们会轮流检查各个端点
尚未检查;我们会在检查端点时读取工具列表
与注册中心里这个分类中所有活跃的服务器比较。只有当发布者拥有该仓库时,星标数才计入。
它在 1 项适用的基础检查中通过了 1 项。我们没有阅读代码。在添加密钥或令牌之前,请先看看这个服务器要求哪些权限,并使用只具备所需权限的密钥。
注册中心没有列出必需的密钥,不过有些服务器会在首次使用时要求你登录。
它在作者的云端运行。通过互联网连接,无需安装,但你发送的内容会经过作者的服务器。
GitHub 仓库最后更新于–,最新版本(1.1.0)于5天前进入注册中心。
支持 MCP 的应用,例如 Claude Desktop、Claude Code、Cursor 和 VS Code。它通过 URL 连接,所以接受远程 MCP 服务器的应用也可以使用,例如开启开发者模式的 ChatGPT。
同一分类中有:MCP YouTube Transcript, Google Analytics MCP, BibiGPT YouTube Video Summarizer
同一分类中其他作者的服务器,按星标数排序。
如何看卡片:★ GitHub 星标i三大支柱:评价 · 检查 · 使用情况i更新 = 最新版本发布到注册中心的时间i
An MCP server retrieving transcripts of YouTube videos
GA4 MCP server for AI agents: schema discovery, server-side aggregation, analysis-ready defaults.
AI video summarizer: summarize and transcribe YouTube, Bilibili, TikTok and podcast videos.
按原文从作者的仓库复制。PopNx MCP 没有审查或背书其内容。HTML、图片和脚本已被移除。链接会离开本站。
Cavyro is a Telegram-native CRM for agents and small sales teams: contacts, companies, deals, pipelines, projects and tasks, campaigns, docs and reports. This repository describes Cavyro's hosted Model Context Protocol server, listed in the MCP Registry as com.cavyro/cavyro.
The server is hosted, so there is nothing to install or run. Point your MCP client at it and it reads and acts on your Cavyro workspace with your permissions.
https://ai.cavyro.com/mcp (Streamable HTTP)https://ai.cavyro.com/.well-known/mcp/server-card.json (full tool surface with input schemas, no credentials needed)server.json · Changes: CHANGELOG.mdThe server source is not published here. The tool surface is generated from the running server and exposed in the server card above, so the card is always the authoritative description of what the server does.
The server exposes a fixed set of nine generic tools over a resource and action registry, instead of one tool per API operation. The client discovers resources and actions on demand, so the tool list stays small while covering over 200 operations across 24 resources.
| Tool | Purpose |
|---|---|
describe_resource | Catalog of all resources and global actions, or one resource's filters, sort fields, enums, operations and actions |
query_resources | List records of any resource with filters, sort, pagination and custom-field filters |
get_resource | Fetch one record by ID |
create_resource | Create a record (custom fields nested under custom_fields) |
update_resource | Change fields on a record |
delete_resource | discard (archive), restore, or destroy a record |
search | Omnibox search across entity types |
perform_action | Any specialized non-CRUD action by slug: move a deal between stages, link records, bulk operations, comments, reports |
load_guidance | On-demand workflow guidance: create flows, custom fields, automations |
Covered domains: deals, contacts, companies, pipelines and stages, projects with folders, task lists, tasks and subtasks, project reports, comments and reactions, activity feed, notifications, a personal My Work queue with mentions, workspaces, members, teams, groups, invitations, custom field definitions, automations, docs with their pages, reports, Google Calendar meetings, read-only Telegram chats and Telegram follow-up reminders.
All tool responses share one envelope, { "data": ..., "pagination": ... }, with pagination present only on paginated list results.
The connection acts as you. An external AI can only see and do what your Cavyro role allows in that workspace.
Every request needs two headers:
| Header | Value |
|---|---|
Authorization | Bearer |
X-Workspace-Id | Your workspace ID |
Create an API token in Cavyro under Settings → API tokens. Tokens can expire after 7, 30 or 90 days, or never for integrations that cannot rotate them. Revoking a token cuts the client off immediately.
There is no OAuth flow. The server answers unauthenticated requests with 401 and a WWW-Authenticate: Bearer realm="cavyro" challenge. MCP is a Pro feature; MCP calls do not consume Cavyro AI credits, the model tokens are paid by your own client subscription.
Replace and in the snippets below. Cavyro's settings page generates the same snippet with your workspace ID filled in.
.vscode/mcp.json, or MCP: Add Server from the command palette:
{
"servers": { "cavyro": { "type": "http", "url": "https://ai.cavyro.com/mcp", "headers": { "Authorization": "Bearer ${input:cavyro-token}", "X-Workspace-Id": "${input:cavyro-workspace}" } } }, "inputs": [ { "id": "cavyro-token", "type": "promptString", "description": "Cavyro API token", "password": true }, { "id": "cavyro-workspace", "type": "promptString", "description": "Cavyro workspace ID" } ] }
The Add to Cursor button above installs the server with placeholder headers; replace them in ~/.cursor/mcp.json.
Cursor: ~/.cursor/mcp.json. Claude Desktop: ~/Library/Application Support/Claude/claude_desktop_config.json.
{
"mcpServers": { "cavyro": { "url": "https://ai.cavyro.com/mcp", "headers": { "Authorization": "Bearer ", "X-Workspace-Id": " " } } } }
claude mcp add --transport http cavyro https://ai.cavyro.com/mcp \
--header "Authorization: Bearer " \ --header "X-Workspace-Id: "
Add the server as a connector in Developer Mode (Plus, Pro, Business or Enterprise) with the URL https://ai.cavyro.com/mcp and the two headers above.
Once connected, ask your assistant in plain language. A few things that work well:
The assistant will typically call describe_resource first to learn the shape of a resource, then query_resources or perform_action. If it needs a multi-step recipe, it calls load_guidance.