Keypup Engineering Analytics

Name: Keypup Engineering Analytics
Author: keypup-io

作者：keypup-io版本 1.0.1i★ –i在云端运行

注册名称io.keypup/engineering-analytics

安装

概览

作者提供的说明（英文原文）

Generate answers & visualizations from your engineering data to track software development health.

PopNx MCP 的三大支柱

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。

用户评价i暂无评价评价功能将在后续阶段与账户功能一起开放
我们的基础检查i1 项中通过 1 项依据公开信息检查；尚未扫描代码
使用情况i非常新★ –

它能帮你做什么

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。

连接方式: 在云端运行
传输方式: Streamable HTTP
分类: 营销与社交媒体
你需要填写：Authorization

安装

选择你使用的 AI 应用，然后复制设置。

安装之前

基础检查：1 项中通过 1 项
在作者的服务器上运行：你向它提出的内容会经过他们的服务器。
需要你自己填写： Authorization

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

在云端运行无需安装任何东西，通过互联网连接

你用的是哪个应用？

ClaudeClaude CodeCursorVS CodeChatGPT

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 …”。

这个地址需要在请求的 header 中放入密钥，而添加连接器的菜单无法设置。请改用 Claude Code、Cursor 或 VS Code。

在终端中运行这条命令

claude mcp add --transport http engineering-analytics https://hq.keypup.io/mcp --header 'Authorization: your-value'

运行前请先填写空着的值。

在 Cursor 中打开

或者自己复制设置

粘贴到项目中的 .cursor/mcp.json，或 ~/.cursor/mcp.json

{
  "mcpServers": {
    "engineering-analytics": {
      "url": "https://hq.keypup.io/mcp",
      "headers": {
        "Authorization": "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。

在 VS Code 中打开

或者自己复制设置

粘贴到项目中的 .vscode/mcp.json

{
  "servers": {
    "engineering-analytics": {
      "type": "http",
      "url": "https://hq.keypup.io/mcp",
      "headers": {
        "Authorization": "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。

这个地址需要在请求的 header 中放入密钥，而添加连接器的菜单无法设置。请改用 Claude Code、Cursor 或 VS Code。

你的选择通过 Cookie 保存在这台设备上，无需账户。

需要你填写的值

Authorization 机密必填 HTTP header
The Bearer API token for authentication. See: https://docs.keypup.io/en/articles/15360547-using-the-mcp-server

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。

它能做什么

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。

基础检查i

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10日

✓ 通过
作者与仓库一致
注册名称已通过域名 keypup.io 验证（注册中心会核实域名所有权）
… 尚未检查
仍在维护
我们还没有从 GitHub 读取这个仓库
… 尚未检查
有许可证
我们还没有从 GitHub 读取这个仓库
… 尚未检查
端点有响应
尚未检查；我们会轮流检查各个端点
… 尚未检查
使用 OAuth 登录
尚未检查；我们会轮流检查各个端点
… 尚未检查
工具描述中没有隐藏指令
尚未检查；我们会在检查端点时读取工具列表

与“营销与社交媒体”中的其他服务器比较i

星标数多于此分类中其他 3708 个服务器的 0%
发布到注册中心的时间比此分类中其他 3708 个服务器的 11% 更新
基础检查结果等于或优于此分类中其他 3708 个服务器的 100%

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。

常见问题

Keypup Engineering Analytics 安全吗？

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。

它需要哪些密钥或代码？

你需要填写：Authorization

它在我的电脑上运行还是在云端？

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。

它还在维护吗？

GitHub 仓库最后更新于–，最新版本（1.0.1）于5个月前进入注册中心。

哪些应用可以使用它？

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。

有什么可以替代它？

同一分类中有：MCP YouTube Transcript, Google Analytics MCP, BibiGPT YouTube Video Summarizer

替代选择

同一分类中其他作者的服务器，按星标数排序。

如何看卡片：↓ 每周下载量i★ 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

MCP YouTube Transcript

An MCP server retrieving transcripts of YouTube videos

作者：jkawamoto正在增长★ 49513天前更新

在你的电脑上运行

安装

Google Analytics MCP

GA4 MCP server for AI agents: schema discovery, server-side aggregation, analysis-ready defaults.

作者：surendranb正在增长↓ 1088/周★ 24413天前更新

在你的电脑上运行

安装

BibiGPT YouTube Video Summarizer

AI video summarizer: summarize and transcribe YouTube, Bilibili, TikTok and podcast videos.

作者：JimmyLv正在增长★ 13713天前更新

在云端运行✓ bibigpt.co

安装

作者的 README

打开 README

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。

Keypup MCP Server

Ask your Keypup engineering data in plain language to track delivery, quality and team workload.

The Keypup MCP (Model Context Protocol) server plugs your Keypup engineering analytics directly into any MCP-compatible AI assistant — Claude, Cursor, Kiro, ChatGPT Desktop, Gemini CLI, VS Code, and others.

Once connected, you ask questions about your engineering activity in plain language and the AI builds and runs the underlying reporting queries for you. No query language to learn, no code to write.

Unlike the GraphQL API, which is designed for developers writing application code, the MCP server is designed to be driven conversationally by an AI on your behalf.

Beta: The Keypup MCP server is currently in beta. The exposed capabilities

are continuously expanded during this phase. Feedback and use cases are

welcome via the in-app chat.

What it can do

The server exposes a focused set of read-only tools that let an AI explore and query your Keypup data:

Discover your companies — list the companies (teams) you belong to.
Explore datasets — browse the available datasets and the fields each one exposes.
Discover formula operators — list the functions and operators available when building metrics, dimensions, and filters.
Run reporting queries — execute aggregated queries against any dataset, with metrics, dimensions (group-by), filters, sorting, and pagination.
Generate a query from natural language — turn a plain-language request into a ready-to-run structured query that the AI can then execute.

The AI orchestrates these tools automatically. A typical flow:

List your companies
Pick the relevant dataset
Look up the fields it needs
Build the query
Run it
Summarize the results

You only ask the question.

Tools

Tool	Description
`list_companies`	List the companies (teams) the authenticated user belongs to. Returns each company's `id`, `name`, `created_at` and `updated_at`. The `id` is required by most other tools.
`list_datasets`	List the datasets (facts) available for querying, each with its `id`, `label` and `description`.
`list_dataset_fields`	List the fields available on a given dataset for a company. Supports filtering by `source` (`NATIVE`/`CUSTOM`) and a regex `pattern` matched against the field id, plus pagination.
`list_formula_operators`	List the operators and functions usable in custom formulas. Supports filtering by `scope` (`DIMENSION`/`METRIC`) and a regex `pattern`, plus pagination. Set `verbose: true` for full per-operator documentation.
`query_dataset`	Run an aggregated report against a dataset. Metrics, dimensions and filters are expressed as text-based formulas, with optional sorting, `limit` and `offset`.
`generate_dataset_query`	Turn a natural-language `prompt` into a structured query compatible with `query_dataset`.

Datasets

The reporting engine runs over the following datasets (facts):

`ISSUES_PULL_REQUESTS` — issues and pull requests.
`ACTIVITY_EVENTS` — transitions on issues/PRs (status changes, assignments, work logged, open/close/reopen).
`COMMENTS` — comments on issues, PRs and reviews.
`COMMITS` — commits attached to pull requests.
`REVIEWS` — pull request reviews.

Example questions

Delivery & throughput

"How many pull requests did we merge each month over the last 6 months?"
"What's our weekly issue closing rate this quarter?"
"How many commits were made per author last month?"

Cycle time & performance

"What's the average time between PR creation and merge over the last 12 weeks?"
"Show me the review turnaround time trend for the last 3 months."
"Which repositories have the slowest cycle time?"

Quality & process

"How many bugs were raised vs. closed each week this quarter?"
"What proportion of our pull requests resolve at least one issue?"
"How many PRs were merged without a review?"

Workload & collaboration

"Who are our most active reviewers this month?"
"How is work distributed across the team right now?"
"How many comments do our pull requests receive on average?"

Open-ended exploration

"Summarize our engineering activity over the last month."
"Compare open vs. closed issues over time."
"What labels are most common on our issues?"

You can refine iteratively: "now break that down by repository", "restrict it to the backend team", and so on.

Setup

Endpoint

https://hq.keypup.io/mcp

The server uses the streamable HTTP transport and supports two authentication methods:

OAuth 2.1 (recommended) — the client registers itself dynamically and you authorize access through your browser. No token to copy or store.
API token — a Keypup API token passed in the Authorization header. Use this when your client does not support OAuth dynamic client registration.

Option 1 — OAuth (recommended)

If your client supports OAuth 2.1 with dynamic client registration (e.g. via a Client ID Metadata Document), point it at the endpoint with no credentials:

"mcpServers": { "keypup": { "type": "http", "url": "https://hq.keypup.io/mcp" } } }

On first connection the client opens a browser window to sign in to Keypup and authorize access. There is no token to generate, copy, or rotate — this is the configuration shipped in this repo's .mcp.json.

If your client does not open an authorization prompt (or fails to register),

it likely does not support dynamic client registration yet. Use the API token

method below instead.

Option 2 — API token

For clients without OAuth dynamic client registration support, add a keypup server entry pointing at the endpoint, with your API token in the Authorization header:

"mcpServers": { "keypup": { "url": "https://hq.keypup.io/mcp", "headers": { "Authorization": "Bearer my_keypup_api_token" } } } }

Notes:

Replace my_keypup_api_token with the token you generate (see below). Keep the Bearer prefix.
The server name (keypup) is arbitrary — name it however you like.

Once saved, restart or reconnect the MCP server in your client. The Keypup tools should appear in the client's tool list, and you can start asking questions.

Generating an API token

Only needed for Option 2 (API token). If you authenticate via OAuth, skip

this section.

The MCP server authenticates with the same API tokens used by the GraphQL API.

Go to Settings → Account and open the API Token tab.
Click Create token.
Set a descriptive name (e.g., "MCP - Claude Desktop") and an expiration. Setting an expiration is strongly recommended.
Select the token scopes. The minimal recommended scopes for the MCP server are:

Reports read (viz:reports:read) — required to explore datasets/fields and run reporting queries.
Companies read (hq:companies:read) — required to list the companies you belong to and resolve the company ID used in queries.

Click Create. The token is shown only once and can be copied for one minute — copy it into your MCP client configuration right away.

Following the principle of least privilege, grant only the scopes you need. The two scopes above are enough to use the full analytics capabilities of the MCP server.

The token grants the same level of data access you already have in the Keypup user interface — queries are automatically scoped to the companies you are a member of.

Security

All Keypup MCP tools are read-only — they never modify your data.
Queries are automatically scoped to the companies you belong to (data isolation).
Treat your API token like a password. Anyone holding it can query your Keypup data within the granted scopes.
Prefer short-lived tokens with an expiration, and rotate them periodically.
Keep tokens out of shared or version-controlled files. Store them in your MCP client's local configuration only.