Spec Driven Development
MCP Server that facilitates spec-driven development workflows, not just Vibe Coding.
제작자가 쓴 설명(영어 원문)
Goal and process maps for humans and AI agents on your own server.
이 세 가지는 절대 하나의 점수로 합치지 않아요. 의견, 측정, 인기는 서로 다른 것이니까요.
이 서버는 아직 쉬운 말로 된 요약을 쓰지 못했어요. 아래는 레지스트리에서 읽어 온 사실이에요.
쓰고 있는 AI 앱을 고른 다음 설정을 복사하세요.
KB_URL, KB_API_KEY비밀로 표시된 값은 내 계정의 열쇠예요. 절대 다른 사람과 공유하거나 채팅에 붙여 넣지 마세요.
필요한 것
어떤 앱을 쓰세요?
위에서 앱을 고르세요. 기억해 두었다가 다음부터는 버튼에 “…에 설치”라고 표시해요.
Claude Desktop → Settings → Developer → Edit Config를 열고 claude_desktop_config.json에 이 내용을 붙여 넣으세요
{
"mcpServers": {
"killbottleneck": {
"command": "npx",
"args": [
"-y",
"killbottleneck-mcp"
],
"env": {
"KB_URL": "your-value",
"KB_API_KEY": "your-value"
}
}
}
}터미널에서 이 명령어를 실행하세요
claude mcp add killbottleneck --env KB_URL=your-value --env KB_API_KEY=your-value -- npx -y killbottleneck-mcp실행하기 전에 빈 값을 채워 주세요.
프로젝트의 .cursor/mcp.json 또는 ~/.cursor/mcp.json에 붙여 넣으세요
{
"mcpServers": {
"killbottleneck": {
"command": "npx",
"args": [
"-y",
"killbottleneck-mcp"
],
"env": {
"KB_URL": "your-value",
"KB_API_KEY": "your-value"
}
}
}
}브라우저가 앱을 열지 물어본 다음, 앱이 설정을 보여 주고 확인을 요청해요. 키 값은 비워 두니 직접 입력하세요.
프로젝트의 .vscode/mcp.json에 붙여 넣으세요
{
"servers": {
"killbottleneck": {
"type": "stdio",
"command": "npx",
"args": [
"-y",
"killbottleneck-mcp"
],
"env": {
"KB_URL": "your-value",
"KB_API_KEY": "your-value"
}
}
}
}브라우저가 앱을 열지 물어본 다음, 앱이 설정을 보여 주고 확인을 요청해요. 키 값은 비워 두니 직접 입력하세요.
이 서버는 내 컴퓨터에서 실행되는데, ChatGPT는 인터넷에 있는 서버에만 연결할 수 있어요. 다른 앱을 고르세요.
선택한 앱은 이 기기의 쿠키에만 저장돼요. 계정은 필요 없어요.
이 설정은 제작자의 레지스트리 정보로 만들었어요. README와 다르면 README를 따르세요.
내 컴퓨터에서 실행돼요. 설치하면 AI 앱에 도구 목록이 나타나요.
누구나 볼 수 있는 정보를 점검했고, 코드는 읽지 않았어요. 해당하지 않는 항목은 개수에서 뺐어요. · 2026년 11월 6일 점검
레지스트리 이름이 도메인 killbottleneck.com 기준으로 확인되었어요(레지스트리가 도메인 소유를 확인해요)
아직 GitHub에서 이 저장소를 읽어 오지 않았어요
아직 GitHub에서 이 저장소를 읽어 오지 않았어요
내 컴퓨터에서 실행되는 서버라 인터넷 엔드포인트가 없어요
내 컴퓨터에서 실행되며, 대신 로컬 설정을 사용해요
내 컴퓨터에서 실행돼요. 도구 목록은 인터넷 엔드포인트가 있는 서버에서만 읽을 수 있어요
레지스트리에서 이 카테고리의 활성 서버 전체와 비교했어요. 스타는 게시자가 저장소를 소유한 경우에만 세요.
해당하는 기본 점검 1개 중 1개를 통과했어요. 코드는 읽지 않았어요. 키나 토큰을 넣기 전에 서버가 어떤 권한을 요구하는지 확인하고, 꼭 필요한 권한만 있는 키를 쓰세요.
입력해야 할 값: KB_URL, KB_API_KEY
내 컴퓨터에서 실행돼요. AI 앱이 필요할 때 실행하고, 데이터는 제작자의 서버를 거치지 않아요(서버 자체가 온라인 서비스를 호출하는 경우는 제외).
GitHub 저장소는 마지막으로 – 변경됐고, 최신 버전(0.63.0)은 2개월 전 레지스트리에 올라왔어요.
Claude Desktop, Claude Code, Cursor, VS Code처럼 MCP를 지원하는 앱에서 쓸 수 있어요. 내 컴퓨터에 Node.js도 있어야 해요.
같은 카테고리에는: Spec Driven Development, Remote Desktop Commander, AI Deprecations
같은 카테고리에서 다른 제작자가 만든 서버예요. 스타 순으로 정렬했어요.
카드 보는 법:★ GitHub 스타i세 기둥: 리뷰 · 점검 · 사용 정도i업데이트 = 최신 버전을 레지스트리에 공개한 날i
MCP Server that facilitates spec-driven development workflows, not just Vibe Coding.
Remote Desktop Commander: use files, terminal and processes on your own computers from any AI app.
AI model shutdown dates, replacements, change feed and webhook alerts for 8 providers.
제작자의 저장소에서 쓰인 그대로 가져왔어요. PopNx MCP가 검토하거나 보증한 내용이 아니에요. HTML, 이미지, 스크립트는 제거했어요. 링크를 누르면 이 사이트를 벗어나요.
killBottleneck
English | Čeština
Website · Documentation · Changelog · Licence
🧪 Public beta. killBottleneck is feature-complete and in beta — the cloud
and the self-hosted version alike; it is one and the same app. What we are
testing here is the self-hosted side: installation, reverse proxies, your
own SMTP, upgrades. Install it (Quick start below), try to break it, and
tell us what happened: bugs → Issues, ideas → Discussions.
v1.0 ships when the beta goes quiet.
A visual picture of your projects, your company and its processes — goal maps that people and AI agents work on together, entirely on your own server: your data never leaves the company. Free to self-host, with REST API and MCP server included. Open in the spirit of open source, just without the right to resell it as a hosted service — see License.
Nothing here phones home. On a default install the server sends no request anywhere, and the app loads nothing from a third-party CDN — fonts included, they are served from your own instance. Everything that could leave your network is something you switch on:
| Outbound request | When it happens | Turn it off |
|---|---|---|
| GitHub Releases API | Version check, from the user's browser — not the server | KB_UPDATE_CHECK=0 |
| The AI endpoint you configured | Only with KB_AI_PROVIDER ≠ none; your own Ollama or any endpoint you choose | KB_AI_PROVIDER=none (default) |
| Google (sign-in, Drive picker) | Only when you configure KB_GOOGLE_* | leave those empty (default) |
There is no telemetry, no analytics and no licence check.
Contents — this README is the full reference; the short version is on the website .
All you need is Docker. Then:
cp .env.example .env # optional — the defaults are fine
docker compose up -d
killBottleneck runs at http://SERVER-IP:8090. Colleagues on the local network just open it in a browser.
The first user to register automatically becomes the administrator. Everyone else can register themselves, or the administrator invites them from Administration (this creates an account with a temporary password to hand over).
A full goal map editor (nodes, edges, statuses, notes), multiple maps per user, comments on goals, sharing maps with colleagues (read / edit), public maps, export to image/PDF.
The “My day” panel (both the home page and the Tasks page): a clickable overview of overdue / today / within a week / blocking others, computed live from your data; name days next to the date; a portrait PNG export for mobile — both full (with task names) and anonymous (names redacted, for social media). Over HTTPS you also get Share… (your phone's native Web Share dialog, no third-party service involved).
Time tracking: a ⏱ timer in the top bar (one click starts an “empty” measurement — the project/client/goal is assigned while it runs or afterwards), a timer on every task and every goal in a map (measuring never changes a status — it is purely supplementary), a left-hand “Time tracking” panel with the records (from–to, retroactive assignment), a “Time worked” dialog in the user menu (today/this week, broken down by project and client), a client registry (project→client, so time adds up per client too), and auto-stop for forgotten timers after 12 h. Inbox behaviour: an unassigned measurement stopped with a note (e.g. “call with the client”) also saves itself as an idea in your stash.
The same instance, opened on a phone, switches to a simplified view: today's tasks, tick them off, add one, and read messages — no map canvas to fight with on a small screen. You can switch back to the full view at any time, and the app can be added to the home screen (over HTTPS) so it behaves like a native one.
More in the Simplified view guide.
The assistant lives in a side panel next to your maps. It plans your day with you (morning briefing, evening planning), sorts notes from a photo, a PDF or a voice note, drafts a whole project after a few questions, prepares a meeting, reviews your week and, for managers, runs a team meeting. It reads your maps itself; every change it proposes is a card you confirm — nothing is written behind your back. E-mails, notes and summaries it writes are kept as documents next to the chat. Besides the assistant there is the wand on a node (sub-steps, milestones, KPIs, risks), an AI project summary and the daily encouragement in My day.
AI is switched on in .env via KB_AI_PROVIDER:
openai — any OpenAI-compatible API: OpenAI, OpenRouter, Groq, Mistral, Together, or your own vLLM / LM Studio / llama.cpp / liteLLM proxy. Set KB_AI_URL=https://openrouter.ai/api/v1 (the base address, usually ending in /v1), KB_AI_TOKEN= and KB_AI_MODEL= . Voice notes are transcribed by the same service when it offers a Whisper / transcribe model; your provider bills you for the requests.api — a remote AI service compatible with the killBottleneck API contract: enter the address and token you got from your provider. No GPU of your own and no maintenance. (Runs the wand, the summary and the encouragement; the assistant needs ollama or openai.)ollama — your own local model: install Ollama, pull a model (ollama pull gpt-oss:20b) and set KB_AI_URL=http://IP:11434 + KB_AI_MODEL=gpt-oss:20b. Everything runs on your side, no data leaves your network. (Voice notes need a speech-to-text service of your own — see below.)custom — your own endpoint honouring the same API contract (the contract is written down here).The assistant uses the same model unless you give it its own (KB_CHAT_PROVIDER/URL/MODEL/TOKEN). Reading photos needs a vision model (KB_VISION_*, or tick *The assistant reads images* in Administration → AI). Voice notes work with any speech-to-text in the OpenAI shape (KB_TRANSCRIBE_PROVIDER=openai + KB_TRANSCRIBE_URL — speaches, whisper.cpp, OpenAI…); the recording itself is never stored. Browsers allow the microphone only over HTTPS (or localhost), and a reverse proxy in front must let request bodies of about 5 MB through (a voice note or a photo travels inside the request). More in the assistant guide.
When AI is used, map data is sent to the endpoint you chose; with none (the default) nothing ever leaves your server.
Daily AI encouragement (a line in the My day panel): 1–2 sentences prioritising “what blocks others → overdue → today”, with the occasional proverb. It is generated in the morning by a cron job (KB_SUMMARY_HOUR, default 6) only for accounts that signed in within the last KB_SUMMARY_ACTIVE_DAYS days (default 14, 0 = everyone); for the rest it is generated when they open the app. Optionally a separate (smaller/faster) model just for the summaries: KB_SUMMARY_PROVIDER/URL/MODEL/TOKEN — without them the general AI configuration above is used. The panel works in full without AI, just without this one line. The AI never enumerates task lists (those are computed from your data and clickable) and task names are sanitised before they go into the prompt.
killBottleneck ships with a built-in MCP server (mcp/): connect your AI assistant to your own instance and maps get built conversationally — “make a map out of these meeting notes”, bulk edits, ticking off what's done. It works the same for self-hosted and hosted instances, only the address differs.
killbottleneck-mcp, so npx fetches it on first use. (Prefer running it from this repository? cd mcp && npm install and use node /absolute/path/mcp/index.js instead of the npx command below.)Claude Code:
claude mcp add killbottleneck \
-e KB_URL=http://SERVER-IP:8090 \ -e KB_API_KEY=kb_user_... \ -- npx -y killbottleneck-mcp
Claude Desktop (claude_desktop_config.json → mcpServers):
{
"mcpServers": { "killbottleneck": { "command": "npx", "args": ["-y", "killbottleneck-mcp"], "env": { "KB_URL": "http://SERVER-IP:8090", "KB_API_KEY": "kb_user_..." } } } }
Tools (20): list_maps, get_map, create_map, add_nodes, update_node, delete_node, list_people, get_portfolio, get_org_structure, calendar events and timed reminders (create_event, list_events, create_reminder) and the rule tools (create_rule, list_rules, update_rule, delete_rule, list_rule_runs, list_rule_templates, save_rule_template, delete_rule_template). A goal with an assignee or a deadline IS a task — there are no separate task records. Assigning an owner through the API shares the map with that person as a collaborator, so the work shows up in their My Day (the response lists who was shared with).
Remote, without anything local: every instance also serves MCP directly at `/mcp` (Streamable HTTP, same keys, same tools) — claude mcp add --transport http killbottleneck https://your-instance/mcp --header "Authorization: Bearer kb_user_..."; the claude.ai connector signs in through OAuth. Details: MCP server.
Security: a key acts as its owner — it sees and edits exactly what the owner can in the app, shared and team maps included (edit/own = full write; work and read = only the status of the owner's own nodes, exactly lik
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