
Keypup Engineering Analytics
io.keypup由 keypup.io 發布官方發布者v1.0.1更新於 Oct 25, 2026
Generate answers & visualizations from your engineering data to track software development health.
概覽
Keypup 的遠端 MCP 伺服器讓 AI 助理用自然語言查詢你的工程分析資料,並產生關於交付、品質與團隊工作量的唯讀報表。
- 可以做什麼
- 列出你所屬的公司(團隊),瀏覽可用資料集及其欄位,包括自訂欄位與附說明的公式運算子。對議題、拉取請求、活動事件、留言、提交與審查執行彙總報表查詢,支援指標、分組維度、篩選、排序與分頁。把自然語言問題轉成結構化查詢並執行。彙總合併吞吐量、週期時間、審查週轉時間、缺陷開啟與關閉比例、審查者工作量等結果。
- 適用情境
- 當你想不寫查詢語句就了解軟體交付、程式碼審查與團隊工作量時使用;也適合讓助理代為草擬並執行 Keypup 報表。適合工程管理者與團隊負責人以對話方式探索 Keypup 資料,而非直接使用 GraphQL API。
- 執行需求
- 遠端 streamable HTTP 端點,無需本機安裝套件。必須驗證:建議使用支援動態用戶端註冊的 OAuth 2.1(瀏覽器登入),或在 Authorization 標頭中以 Bearer 前綴傳入 Keypup API 權杖。建議權杖包含 viz:reports:read 與 hq:companies:read 權限範圍。需要一個屬於相關公司的 Keypup 帳戶。
安裝
在 SourceWeft 中
- 開啟 儀表板中的 Keypup Engineering Analytics,將其新增到工作區。
- 連接 所需的 憑證。它們會被私密儲存,絕不會在公開頁面上展示。
- 為需要使用其工具的對話啟用該服務。
Web executable,透過 Streamable HTTP。 遠端服務在工作區中設定後即可從網頁執行環境執行。
交給代理程式安裝
把這段提示詞貼上到 Claude Code、Codex、Cursor 等代理程式,或 SourceWeft 對話中。代理程式會讀取該伺服器的安裝指南,先向你展示發布者、端點和登入方式,經你同意後再把它新增到你的客戶端。金鑰不會寫進任何命令。
閱讀 https://sourceweft.com/mcp/io.keypup%2Fengineering-analytics/install.md,並按其中說明把這個 MCP 伺服器接入我的客戶端。修改任何設定之前,先告訴我它的發布者、端點和登入方式,等我確認。不要把 API 金鑰寫進命令或我會提交的檔案;問我金鑰應從哪裡取得。其他 MCP 客戶端
把它新增到你客戶端的 mcpServers 設定中。 客戶端會在首次連接時完成 OAuth 登入。
{
"mcpServers": {
"keypup-engineering-analytics": {
"type": "http",
"url": "https://hq.keypup.io/mcp"
}
}
}README
Keypup MCP Server
[Install in VS Code] [Install in Cursor] [Add to Kiro]
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
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
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
Authorizationheader. 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:
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:
Notes:
- Replace
my_keypup_api_tokenwith the token you generate (see below). Keep theBearerprefix. - 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.
- Reports read (
- 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.
Resources
來源:README.md,提交 03c8bc2
工具
0版本歷史
1- v1.0.1最新Sep 16, 2026
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