Spec Driven Development
MCP Server that facilitates spec-driven development workflows, not just Vibe Coding.
제작자가 쓴 설명(영어 원문)
Connect RStudio to AI assistants for interactive R coding and data analysis.
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": {
"clauder": {
"command": "uvx",
"args": [
"clauder-mcp"
]
}
}
}터미널에서 이 명령어를 실행하세요
claude mcp add clauder -- uvx clauder-mcp프로젝트의 .cursor/mcp.json 또는 ~/.cursor/mcp.json에 붙여 넣으세요
{
"mcpServers": {
"clauder": {
"command": "uvx",
"args": [
"clauder-mcp"
]
}
}
}브라우저가 앱을 열지 물어본 다음, 앱이 설정을 보여 주고 확인을 요청해요. 키 값은 비워 두니 직접 입력하세요.
프로젝트의 .vscode/mcp.json에 붙여 넣으세요
{
"servers": {
"clauder": {
"type": "stdio",
"command": "uvx",
"args": [
"clauder-mcp"
]
}
}
}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 IMNMV 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(0.16.1)은 37일 전 레지스트리에 올라왔어요.
Claude Desktop, Claude Code, Cursor, VS Code처럼 MCP를 지원하는 앱에서 쓸 수 있어요. 내 컴퓨터에 Python (uv)도 있어야 해요.
같은 카테고리에는: Spec Driven Development, Remote Desktop Commander, Zoo MCP
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.
An MCP server that provides access to the Zoo API for various CAD operations and tools.
제작자의 저장소에서 쓰인 그대로 가져왔어요. PopNx MCP가 검토하거나 보증한 내용이 아니에요. HTML, 이미지, 스크립트는 제거했어요. 링크를 누르면 이 사이트를 벗어나요.
ClaudeR - The Modern Researcher's Toolkit
Connect a live R or Python session, in RStudio or Positron, to Claude Code, Codex, or any MCP-based LLM agent. For interactive analysis, multi-agent orchestration, and automated manuscript auditing.
ClaudeR connects MCP-configured LLM agents, like Claude Code or Codex, to the R session you are already working in. The agent runs code where your cleaned data and fitted models actually are, sees the plots it draws, reads the objects in your environment, and can edit the file open in your editor. A companion Python package does the same for a live Python session, and both work in RStudio and Positron.
Several agents can share one session and divide the work between them, or you can run separate sessions side by side so each agent works on its own dataset without touching the others.
It covers much of what a paid research AI tool does, with the work happening in your own session and a record of every line an agent ran.
# Install
if (!require("devtools")) install.packages("devtools") devtools::install_github("IMNMV/ClaudeR")
library(ClaudeR) install_clauder() # For Claude Desktop / Cursor install_cli(tools = "claude") # For Claude Code CLI
claudeAddin()
AI agents: See llms-install.md for automated setup instructions.
| Single agent via Claude Desktop App | Multi-agent: Codex + Claude Code via CLI | GPT 5.4 Codex: Data analysis + Quarto report |
|---|---|---|
Recent Updates (click to expand)
agy) replaced it. install_cli(tools = "gemini") and the gemini option of run_annotation_job are gone, and both now point you to agy instead.run_annotation_job now starts each Claude call with no tools, MCP servers, hooks, CLAUDE.md, plugins or slash commands, from an empty folder, so the model labelling a row sees nothing but that row. Older Claude Code versions get whichever of these options they have. Codex calls also start from an empty folder. Every reply is kept word for word in {name}_annotating_raw.jsonl. A reply that isn't valid JSON or doesn't fit the schema is asked again, up to three tries. Hitting your plan's usage limit stops the job with every saved row kept, and running it again picks up where it stopped. Two new options: no_api_key = true removes API keys from each call so the CLI uses your subscription login, and batch_size sends several rows per call for very large jobs (the default stays 1, so no row can influence another). A crash no longer leaves a job reading "running" forever, and the working file is never left half written.restore_session("before_filter") failed with "checkpoint not found" because only the stamped file name was accepted; the label a checkpoint was saved with now works, and a miss lists the checkpoints that exist.start_console_logging() before starting a session also said logging was on while writing nowhere; it now says commands are recorded once a session starts, and they are. Starting a session prints where its log is, and your commands are no longer appended to the previous session's log while the server is stopped.get_async_result now waits for the job and returns the moment it finishes (up to wait_s seconds, default 300), so a 20-minute model fit takes a handful of calls instead of about 120. When output is too long to send in full, the whole thing is saved to a file and the agent is told where, so it can read the part that was cut instead of running the code again. The addin's Agents panel now hides agents not seen in a day, like cr_roster() already did, so a freshly opened session no longer lists agents from weeks ago.start_console_logging() in the console worked. Cause: globalCallingHandlers() (used to observe warnings and messages) can only be called with an empty handler stack, and inside the Shiny observer that handles the checkbox it errors, which aborted the function before the console callback was registered. The callback is now registered first and unconditionally; the handlers are installed only when logging is started from the console, and otherwise warnings are read from last.warning after each command. Started from the addin, message() output is the one thing not captured, and the addin says so.execute_r_with_plot, saving roughly 4,500 tokens per image when you mostly want text. Your Plots pane is unaffected either way. "Tools available to the agent" hides whole groups you are not using (editor, session, coordination, background jobs, manuscript audit); all 41 tools cost about 8,600 tokens of schema on every request, and a programming-only session needs about 1,200 of that. Both settings are per session and take effect without a restart, though some MCP clients only notice a changed tool list on reconnect. Separately, a bug fix: p <- ggplot(...) returned a full image to the agent even though assignment displays nothing, so the agent was sent plots that never appeared in your Plots pane. Capture now requires the plot to have actually been shown.cat(), progress output, and print() called inside a function. Running f() showed the call but not what f() printed, which is exactly the context an agent needs when you hand it an error you hit yourself. Standard output is now teed to the log and grouped under the command that produced it, alongside the warnings, messages, and errors already captured. Your console still shows everything as normal.tools::pskill(pid, signal = 0). That is the standard idiom on Unix, but on Windows pskill always calls TerminateProcess regardless of signal, so the check killed the session it was asking about, then reported it as alive and left the stale discovery file in place. Starting a server could therefore terminate another RStudio session and leave agents routed to a dead port. Liveness is now probed without signalling. Separately, registering a session whose name is already held by a different live session now warns instead of silently overwriting its discovery file, which had left agents holding the wrong port and token.start_console_logging() / stop_console_logging().modify_code_section and insert_text now save to disk by default and report saved_to_disk, so an agent no longer believes it wrote a file when the change sat unsaved in the buffer. Bounded replacements may change the line count (the old equality constraint is gone). Both tools accept a path to target a specific file, open and focus it, and refuse to edit a different document instead of failing silently. get_active_document now reports the path, the document id, and whether the buffer differs from disk, so buffer state and file state stop being confused for each other. New suggest_edit tool: the agent proposes a change and waits for the user to approve it, using rstudioapi::showEditSuggestion() when the installed rstudioapi provides it, and otherwise staging the edit unsaved so the user accepts by saving or rejects with undo.앞부분만 보여 드려요. 나머지는 저장소에서 읽어 보세요