Spec Driven Development
MCP Server that facilitates spec-driven development workflows, not just Vibe Coding.
제작자가 쓴 설명(영어 원문)
Solve it once. From then on, your AI knows. Experience for coding agents, hosted in the EU.
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.
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": {
"cachly-dev": {
"command": "npx",
"args": [
"-y",
"@cachly-dev/mcp-server"
]
}
}
}터미널에서 이 명령어를 실행하세요
claude mcp add cachly-dev -- npx -y @cachly-dev/mcp-server프로젝트의 .cursor/mcp.json 또는 ~/.cursor/mcp.json에 붙여 넣으세요
{
"mcpServers": {
"cachly-dev": {
"command": "npx",
"args": [
"-y",
"@cachly-dev/mcp-server"
]
}
}
}브라우저가 앱을 열지 물어본 다음, 앱이 설정을 보여 주고 확인을 요청해요. 키 값은 비워 두니 직접 입력하세요.
프로젝트의 .vscode/mcp.json에 붙여 넣으세요
{
"servers": {
"cachly-dev": {
"type": "stdio",
"command": "npx",
"args": [
"-y",
"@cachly-dev/mcp-server"
]
}
}
}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 GitHub 계정 cachly-dev 기준으로 확인되었고, 이 계정이 저장소를 소유하고 있어요
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.
레지스트리에 필수 키가 나와 있지 않아요. 다만 처음 쓸 때 로그인을 요구하는 서버도 있어요.
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(0.10.231)은 어제 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, debuff
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.
Let coding agents play, test and tune your 2D web game: live state, explained errors, replays, fuzz.
제작자의 저장소에서 쓰인 그대로 가져왔어요. PopNx MCP가 검토하거나 보증한 내용이 아니에요. HTML, 이미지, 스크립트는 제거했어요. 링크를 누르면 이 사이트를 벗어나요.
Solve it once. From then on, your AI knows.
Every fix, every decision and every dead end becomes experience your AI uses at
the right moment. You build faster, and your team builds on what you figured out.
@cachly-dev/mcp-server connects cachly to Claude Code, Cursor, GitHub Copilot, Windsurf, Cline, Continue.dev, Zed, Codex CLI and Gemini CLI over MCP. It offers 124 MCP tools. Your editor lists 27 entries by default and reaches the rest through one of them, cachly_tool (details under MCP Tools below). Your AI stores a lesson after a fix, and finds it again when the same problem returns. Your Brain (the store of your lessons) lives on EU servers.
/plugin marketplace add cachly-dev/cachly-mcp
/plugin install cachly-brain@cachly
There is no JSON to write, no path to set and no key to copy. Without a key, cachly creates a test Brain on EU servers when the session starts (the plugin hook does it; the first tool call does it if the hook did not). The test Brain has 14 days of the Solo tier. After that it runs on the free tier with the same key.
Check it with claude mcp list. You should see plugin:cachly-brain:cachly … ✔ Connected. claude plugin details shows MCP servers (0) even when the server runs, because it does not count them.
The plugin adds three Claude Code hooks:
| Hook | What it does |
|---|---|
SessionStart | Gives your AI a short briefing: the 5 most-reused lessons (on a new Brain: starter tips) |
UserPromptSubmit | Puts the lessons that match your prompt next to it |
Stop | Saves a lesson when the AI's last message clearly reports a fix. It skips messages that say something failed or is unclear. It tells you nothing when it saves. |
Turn on auto-update once: /plugin → Marketplaces → cachly → Enable auto-update. Claude Code leaves it off for marketplaces outside Anthropic's own. The MCP server updates itself on every start, but the hooks come with the plugin and update only with it. If an update is waiting, the first session_start of a session says so in one line.
npx @cachly-dev/mcp-server@latest claim
You sign in once in the browser. The test Brain then moves into your account with all its lessons. Every test key is revoked, and you get a new key that is stored where your editor and hooks look for it. The Brain restarts once, which takes about 20 seconds. If you already have a free Brain, the one-Brain limit of the free tier applies and the move is refused with a message.
npx @cachly-dev/mcp-server@latest autopilot
autopilot finds the editors you use and writes the MCP config for each. It also writes the rule file each editor reads in every chat (CLAUDE.md, AGENTS.md, .cursor/rules/cachly.mdc, .github/copilot-instructions.md, .windsurfrules, .clinerules; GEMINI.md, .junie/guidelines.md and .continue/rules/cachly.md where you use Gemini CLI, Junie or Continue), which tells your AI to check its memory before a task. It adds a git hook that learns from your commits and fills your Brain from your git history. You sign in once in the browser (a device code, no password, no credit card), so the Brain belongs to your own account from the start. Restart your editor afterwards.
You can also add the server by hand (see Manual Setup). Without a key, the first tool call creates the same test Brain as in Claude Code. Without a rule file, Claude Code still starts each task with smart_recall, because the server's instructions ask for it. Gemini CLI also passes them on. Some editors do not (Cline, Continue and Zed, for example); there, the rule file from autopilot is what makes your AI use its memory.
Already inside Claude, Cursor or Copilot? Paste this to your AI:
`
Set up cachly for this project. Run: npx @cachly-dev/mcp-server@latest autopilot
Follow the browser sign-in, then restart the editor.
`
Install [cachly Brain](https://marketplace.visualstudio.com/items?itemName=cachly-dev.cachly-brain). It creates your Brain on first start, with no account form.
Install [Cachly Brain](https://plugins.jetbrains.com/plugin/32059-cachly-brain) from the JetBrains Marketplace (IntelliJ IDEA, PyCharm, GoLand, WebStorm, Rider). The status bar, the Brain health and the lessons view live in the IDE. The source is at cachly-dev/cachly-intellij. The JetBrains plugin guide shows how Claude Code, Junie and JetBrains AI Assistant get the Brain over MCP.
The server is listed in the official [MCP Registry](https://registry.modelcontextprotocol.io/v0/servers?search=cachly) as io.github.cachly-dev/mcp-server. Clients that browse the registry find it by name. The entry points at this npm package.
| Guide | What you find there |
|---|---|
| Claude Code hooks: which event to use when | SessionStart, UserPromptSubmit, PreToolUse, Stop and more, sorted by when they fire. What each one can block, add and change. |
| A UserPromptSubmit hook for Claude Code | The stdin JSON, additionalContext, a working Node script and five traps to avoid. |
| The same hooks in VS Code and Copilot CLI, Codex CLI and Gemini CLI | Which event takes context in each editor, and the exact file setup writes there. |
| Comparisons: Supermemory, Zep, claude-mem, Basic Memory, mem0, Letta | Side by side, each row with its source and a visible date, including when the other product fits better. |
| EU-hosted MCP server: what leaves your machine | Where data lands: storage in Germany, embeddings in the EU, the network edge, and a checklist. |
| Lessons learned for coding agents | How to write a lesson your agent finds again: symptom, decisive fact first, what failed, files, a check. With examples and a LESSONS.md. |
| JetBrains plugin guide | What the plugin does, and how Claude Code, Junie and JetBrains AI Assistant use the Brain. |
| Repo X-ray | Enter a public GitHub repo. In a few seconds you see how many problems its team has solved, and which ones more than once. |
smart_recall looks up the lessons that match. It combines keyword search (BM25), meaning-based search and the causal graph, in 11 languages.causal_trace walks from the symptom to the cause and to the fix that worked.brain_predict lists failure patterns from past incidents.To see what your AI would know before you install anything, run npx @cachly-dev/mcp-server@latest demo in a project folder. It reads your git history and prints your fixes, patterns and decisions. Nothing leaves your machine.
Our agreement with you. You can leave and take your data: npx @cachly-dev/mcp-server@latest export writes every lesson to lessons.md (to read) and lessons.jsonl (to reuse). Code excerpts are stored only if you call index_project yourself, and only on your own EU instance. Read what leaves your machine before you decide.
Your editor sees 27 entries by default: the daily tools (26 of them) and one dispatcher, cachly_tool. This is on purpose. The full list of 124 costs about 28,000 tokens in every request. The 27 listed entries cost about 8,000. All other tools (98 of them) run through the dispatcher:
cachly_tool(tool: "team_roster") run any of them by name
cachly_tool(tool: "team_roster", describe: true) get its schema first
Nothing is unreachable. The server dispatches by name, so team_roster called directly still works. Set CACHLY_ALLE_WERKZEUGE=1 to list all 124. Set CACHLY_PROFILE=lean to list 8 plus cachly_tool, or CACHLY_PROFILE=recall to list the 3 read-only tools.
| Tool | What it does |
|---|---|
| `session_start` | Full briefing: last session, open failures, recent lessons, Brain health |
| `session_end` | Save what you built; extract lessons from the summary and the git log |
| `learn_from_attempts` | Store a structured lesson after any fix, deploy or discovery (with author, visibility) |
| `recall_best_solution` | The known fix for a topic, with its success and failure history |
| `smart_recall` | Hybrid BM25 + semantic + causal-graph search, 11 languages, quality-reranked |
| `remember_context` | Keep architecture findings, decisions and file summaries |
| `compact_recover` | Recover your context after you hit the context-window limit |
| Tool | What it does |
|---|---|
team_learn / team_recall | Share lessons across the team with author attribution |
team_confirm | A reviewer confirms a lesson (senior or peer); confirmed lessons rank higher in recall |
team_assign_role / team_roster / team_whoami | Roles (admin, reviewer, contributor, viewer), enforced once an admin is set |
team_audit | Admin-only governance trail: every role change and lesson confirmation |
| `brain_who_knows` | Find your team's experts on a topic, ranked from authorship |
| `brain_file_map` | Experts and lessons per file, before you touch it |
| `team_expertise_map` | The team's skills matrix in one table |
| `brain_collab_pairs` | Who works with whom, with bus-factor alerts |
| `brain_portability` | Config snippets for 7 MCP clients, so the same Brain serves any model |
| `skill_gaps` | Knowledge blind spots: unresolved failures, missing attribution |
| `brain_coverage` | A 0–100 health score for the knowledge of your codebase |
madc_deliberate | Specialist AI agents vote to resolve contradictory lessons |
memory_crystalize | Distill all lessons into a Crystal for instant team context |
team_crystallize | A Team Crystal: fixes that 2 or more teammates reached independently |
| Tool | What it does |
|---|---|
| `causal_trace` | Root-cause analysis through the causal knowledge graph |
`brain_predict` / brain_predict_failures | Predict likely failures before they happen |
| `brain_from_git` | Fill people, file
앞부분만 보여 드려요. 나머지는 저장소에서 읽어 보세요