
Standup from your activity
io.github.sathvic-kollu由 GitHub 账号 sathvic-kollu 发布v0.1.0更新于 Nov 3, 2026
Generate your daily standup from real GitHub, Jira, Linear, and Slack activity.
概览
本地运行、只读的 MCP 服务器,根据 GitHub、Jira、Linear 和 Slack 的真实活动按工作项生成每日站会草稿。
- 可以做什么
- 生成站会草稿(昨天/今天/阻塞),按工作项归并,默认取上一个工作日并自动覆盖周末
- 按严重程度扫描阻塞项,综合“被阻塞/等待中”措辞、待审 PR、停滞的进行中工单和求助请求
- 把一周活动汇总为“已交付/进行中”,用于一对一沟通和周报
- 按时间倒序查看跨所有已连接工具的活动摘要
- 列出已接入的数据源,或说明当前是否在使用内置演示数据
- 适用场景
- 适合每天要写站会或每周要写状态汇报、且工作记录已存在于 GitHub、Jira、Linear 或 Slack 的人,用来得到可编辑的事实草稿,而不必凭记忆重写。也便于发现自己忘记提及的阻塞。
- 运行要求
- 通过 stdio 在本地运行,通常用 npx 从 npm 包启动,需要 Node.js。各数据源凭据可选:GITHUB_TOKEN,JIRA_BASE_URL 加 JIRA_EMAIL 与 JIRA_API_TOKEN,LINEAR_API_KEY,以及 SLACK_TOKEN(机器人令牌还需 SLACK_CHANNELS);STANDUP_NAME 可选。未配置任何凭据时使用演示数据。需要能访问这些工具 API 的网络。
安装
在 SourceWeft 中
- 打开 控制台中的 Standup from your activity,将其添加到工作区。
- 为需要使用其工具的对话启用该服务。
Desktop only,通过 STDIO。 STDIO 服务会启动本地进程,因此需要 SourceWeft 桌面宿主。
其他 MCP 客户端
参照 仓库 中的启动说明。
README
standup-mcp
Generate your daily standup from what you actually did, across GitHub, Jira, Linear, and Slack.
[npm version] [license] [node] [MCP]
Every standup, you stop and reconstruct yesterday from memory: which commits, which PR, which ticket you moved, that Slack thread where you said you were stuck. The work is already recorded across your tools. Re-typing it is the chore everyone hates, so updates come out vague ("worked on the thing") and real blockers go unsaid.
standup-mcp reads that activity back and writes the draft for you. You ask your AI assistant "what should I say at standup today?" and it answers from your real GitHub, Jira, Linear, and Slack activity: grouped by work item, in concrete deltas, with blockers it noticed on its own.
It is a standard Model Context Protocol server, so it works in any MCP client (Claude, Cursor, Cline, and more) on any model. It is local and read-only: nothing about your activity leaves your machine except the calls to those tools' own APIs, and it needs no AI API key of its own. The host client supplies the model.
See it in 30 seconds (no accounts needed)
The server ships with a realistic demo dataset and runs against it automatically when no credentials are set, so you see the output before connecting anything:
That prints a full standup from a synthetic day across all four tools. Here is the headline tool:
Notice the GitHub commits, the GitHub PR, and the Jira move all collapsed under PROJ-412, and the blocker was lifted from a Slack message on its own. Nobody typed any of that.
(Running npx -y standup-mcp with no flag starts the MCP server on stdio, which is what an MCP client launches. Use --demo to see output in a plain terminal.)
What it does
Five tools. Every one defaults to the last working day, and Monday automatically reaches back across the weekend, so you rarely pass arguments.
What you can ask
You talk to it in plain language through your AI client:
- "What should I say at standup today?"
- "Give me Monday's standup covering the weekend."
- "What is blocking me right now?"
- "Summarize what I shipped this week for my 1:1."
- "What did I actually do yesterday?"
Principles it follows
- Only observed activity. It reports commits, PRs, ticket moves, and messages that exist. It never invents progress to fill a quiet day; a quiet day is reported as one.
- Grouped by work item, not by tool. Reviewers think in tickets, so the commit, the PR, the Jira move, and the Slack thread for PROJ-412 become one line, not four.
- Blockers from signals, not self-report. People forget to say they are stuck, so it infers it.
- A draft, not an auto-post. You get editable markdown to paste wherever your team already does standup (Slack, Geekbot, a doc, a ticket). It does not post on your behalf.
Example: blocker_scan
Example: weekly_summary
Privacy
This is the part most tools gloss over. standup-mcp is built so that using it does not feel like installing surveillance on yourself:
- Local. It runs on your machine, inside your AI client. There is no standup-mcp server or account.
- Read-only. Every token it asks for is used only to read your activity. It never writes, posts, or moves anything.
- No AI key, no third party. It makes no LLM calls of its own. Your activity is sent only to the APIs of the tools you connect, and to your existing AI client's model. It is not sent to me or anyone else.
- It is yours, not your manager's. It generates your own update for you to review and edit, not a feed of your activity for someone else.
Connect your tools
Set any subset. Whatever you configure, it uses; with nothing set, it stays in demo mode. Restart the server after changing these.
Verify your connections before wiring it into a client:
It prints, per source, the authenticated identity or a clear error.
Connect your AI client
standup-mcp speaks the Model Context Protocol, so any MCP-capable client can use it, whichever model is behind it: Claude Desktop, Claude Code, Cursor, Cline, Continue, Zed, Windsurf, and more. The server uses no AI API key of its own.
Claude Desktop
Add this to claude_desktop_config.json (Settings, Developer, Edit Config), then restart Claude Desktop:
Leave the env block out entirely to run in demo mode first. Include only the sources you use.
Claude Code
Cursor, Cline, Continue, Zed, Windsurf, and others
These read the same mcpServers JSON as Claude Desktop, in the client's own MCP config. Use the block above. The server is identical; only the model driving the client differs.
How it works
The design goal is a clean seam between each tool and the standup logic.
- One activity model. GitHub commits, Jira moves, Linear state changes, and Slack messages all normalize to a single
ActivityEvent. Nothing downstream knows which tool a fact came from, which is exactly what lets it group by work item across tools. - One provider, many sources. Each source is an independent read-only client behind a common interface. The aggregator fans out to whichever are configured and tolerates any one failing, so a misconfigured Slack token never sinks your standup.
- Pure-function engine. Grouping, blocker detection, and the draft are pure functions over normalized events. They run identically on demo data and live data, and the tests run them directly.
- No model in the server. The server assembles a factual draft; your AI client phrases it. That is why it needs no AI key and why it can promise it never invents work.
What has been verified
- All five tools run end to end over real MCP stdio (
npm test). - The parse heuristics (work-item keys, blocker language, noise filters) are unit tested (
test/normalize.test.ts). - The engine is unit tested, including the Monday-covers-the-weekend window, work-item grouping, blocker severity, and the assembled draft (
test/draft.test.ts). - The whole engine is exercised against the demo dataset and reconciles across tools (
npm run smoke).
The demo dataset proves the engine. It does not prove each live client against every real account shape, which is why the parse layer is unit tested separately and each source is kept small and tolerant. Run --check to confirm your own connections, and if a response shape does not parse cleanly on your account, open an issue.
Roadmap
- A
team_standupview that rolls up several people for the PM running the meeting - Calendar sources (meetings as activity, so a meeting-heavy day reads honestly)
- GitLab and Bitbucket
- Cycle-time and "what changed since I last looked" digests
- OAuth flows in addition to tokens
Built by Sathvic Kollu
I run delivery for SaaS and fintech teams, and I build tools like this with Claude Code. If this saves you the daily standup chore, I would like to hear how you use it.
- Website: sathvickollu.com
- LinkedIn: linkedin.com/in/sathvic-kollu
Issues and pull requests are welcome.
License
MIT. See LICENSE.
来源:README.md,提交 8e30b8d
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0版本历史
1- v0.1.0最新Sep 16, 2026
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