MCP YouTube Transcript
An MCP server retrieving transcripts of YouTube videos
作者提供的说明(英文原文)
Analyze Convai sessions, latency, reliability, usage, and provider telemetry via API key.
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。
CONVAI_API_KEY, CONVAI_ANALYTICS_BASE_URL标为机密的值是你账户的钥匙。千万不要分享给别人,也不要粘贴到聊天里。
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 …”。
打开 Claude Desktop → Settings → Developer → Edit Config,把下面的内容粘贴到 claude_desktop_config.json
{
"mcpServers": {
"convai-analytics": {
"command": "npx",
"args": [
"-y",
"@convai/analytics-mcp"
],
"env": {
"CONVAI_API_KEY": "your-value",
"CONVAI_ANALYTICS_BASE_URL": "your-value"
}
}
}
}在终端中运行这条命令
claude mcp add convai-analytics --env CONVAI_API_KEY=your-value --env CONVAI_ANALYTICS_BASE_URL=your-value -- npx -y @convai/analytics-mcp运行前请先填写空着的值。
粘贴到项目中的 .cursor/mcp.json,或 ~/.cursor/mcp.json
{
"mcpServers": {
"convai-analytics": {
"command": "npx",
"args": [
"-y",
"@convai/analytics-mcp"
],
"env": {
"CONVAI_API_KEY": "your-value",
"CONVAI_ANALYTICS_BASE_URL": "your-value"
}
}
}
}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": {
"convai-analytics": {
"type": "stdio",
"command": "npx",
"args": [
"-y",
"@convai/analytics-mcp"
],
"env": {
"CONVAI_API_KEY": "your-value",
"CONVAI_ANALYTICS_BASE_URL": "your-value"
}
}
}
}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。
这个服务器在你的电脑上运行,而 ChatGPT 只能连接互联网上的服务器。请选择其他应用。
你的选择通过 Cookie 保存在这台设备上,无需账户。
CONVAI_ANALYTICS_BASE_URL env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。
它在你的电脑上运行;安装后,工具列表会显示在你的 AI 应用中。
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7日
注册名称已通过 GitHub 账户 Conv-AI 验证,该账户拥有此仓库
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
它在你的电脑上运行,没有互联网端点
在你的电脑上运行,改用本地设置
它在你的电脑上运行;我们只能读取互联网端点的工具列表
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。
你需要填写:CONVAI_API_KEY
它在你的电脑上运行。你的 AI 应用会在需要时启动它,数据不会经过作者的服务器(除非它自己调用在线服务)。
GitHub 仓库最后更新于–,最新版本(0.2.5)于6个月前进入注册中心。
支持 MCP 的应用,例如 Claude Desktop、Claude Code、Cursor 和 VS Code。你的电脑上还需要有 Node.js。
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。
Local stdio MCP server for Convai Analytics.
This package is the recommended agent interface for Convai Analytics. It lets MCP-capable agents answer questions about your Convai sessions, latency, reliability, usage, and provider/model performance without writing custom SDK scripts. It wraps the public @convai/analytics TypeScript SDK, uses CONVAI_API_KEY, and returns structured JSON plus Vega-Lite chart specs.
Package: @convai/analytics-mcp
Official MCP Registry name: io.github.Conv-AI/convai-analytics-mcp
export CONVAI_API_KEY="ck_live_your_key_here"
npx -y @convai/analytics-mcp@latest
Optional:
export CONVAI_ANALYTICS_BASE_URL="https://analytics-api.convai.com/v1/analytics"
Leave CONVAI_ANALYTICS_BASE_URL unset for production. Do not put the API key in source code, committed MCP config, screenshots, or logs.
After adding or changing MCP config, restart Claude Desktop, Claude Code, Cursor, Codex, or your MCP host so it reloads the server command and CONVAI_API_KEY.
{
"mcpServers": { "convai-analytics": { "command": "npx", "args": ["-y", "@convai/analytics-mcp@latest"], "env": { "CONVAI_API_KEY": "ck_live_your_key_here" } } } }
After saving this config, quit and reopen Claude Desktop so it starts the new MCP server.
Use the same stdio command:
{
"command": "npx", "args": ["-y", "@convai/analytics-mcp@latest"], "env": { "CONVAI_API_KEY": "ck_live_your_key_here" } }
For Claude Code, Cursor, Codex-compatible clients, and other stdio MCP hosts, restart the client or start a new session after adding or changing MCP config.
The server is customer-facing and public-safe by design:
@convai/analytics.CONVAI_API_KEY.Core and account tools:
get_summaryget_metrics_cataloglist_sessionsget_session_timelineget_interaction_traceLatency tools:
get_p95_latency_over_timeget_latency_percentile_seriesget_latency_percentile_chartget_latency_threshold_chartget_latency_heatmap_chartget_component_latency_breakdowngenerate_component_latency_chartfind_component_latency_bottlenecksgenerate_interaction_waterfallexplain_slow_sessionReliability tools:
get_error_trendgenerate_error_trend_chartget_error_breakdowngenerate_error_breakdown_chartget_dropped_error_persist_trendgenerate_dropped_error_persist_chartgenerate_reliability_summary_chartgenerate_reliability_trends_chartThese reliability tools use error and dropped-persistence metrics that are available today. If an agent asks for a status distribution, only use group_by=status when the returned rows contain real non-empty status groups; otherwise report that status-tagged aggregate telemetry is not available for that range.
Usage and concurrency tools:
get_usage_trendsgenerate_usage_trends_chartget_character_usage_leaderboardgenerate_character_usage_chartgenerate_session_duration_scatterestimate_active_session_concurrencygenerate_active_session_concurrency_chartgenerate_peak_concurrency_chartProvider and model tools:
get_tts_provider_attributiongenerate_tts_provider_chartget_llm_model_latencygenerate_llm_model_latency_chartcompare_providersPlan-gated advanced tools:
detect_regressionsadvanced_queryPlan-gated tools return typed MCP tool errors for 402/403 responses with the required plan in the message.
The server exposes prompt templates for common customer questions:
why_was_this_session_slowaggregate_latency_distributionp95_latency_trendcomponent_bottleneckstrace_explanationerror_rate_trendsprovider_comparisonusage_summaryconvai://analytics/docs/conceptsconvai://analytics/docs/metrics-referenceconvai://analytics/docs/authenticationconvai://analytics/catalogThe catalog resource fetches the caller's account catalog when CONVAI_API_KEY is available.
How many interactions and unique end users did I have in the last 30 days?
Break it down by character and generate a usage trend chart.
Show aggregate P50/P95/P99 voice.user_to_bot_latency for production readiness sign-off.
Add a 3000 ms p95 threshold line and summarize the worst buckets.
Explain why session s_123 was slow and generate an interaction waterfall if there is a traceable interaction.
Show reliability trends, errors by component, and dropped error-persistence events.
Estimate active session concurrency and show the peak concurrency chart.
From the repo root:
make mcp-install
make mcp-lint make mcp-test make mcp-build make mcp-smoke
Live smoke against production with your own key:
export CONVAI_API_KEY="ck_live_your_key_here"
export CONVAI_ANALYTICS_BASE_URL="https://analytics-api.convai.com/v1/analytics" export CONVAI_ANALYTICS_E2E_RANGE="last_30d" cd packages/mcp npm run e2e:prod
The live smoke accepts empty data for accounts without recent traffic and treats plan-gated 402/403 responses as valid gated results.
The canonical MCP registry manifest is server.json. The server is published to npm and the Official MCP Registry. Additional directories and marketplaces such as GitHub MCP Registry, Smithery, Glama, PulseMCP, mcp.so, and Docker MCP Catalog can reference the same package and registry metadata.