Agent-Native Calendar
Agent-Native Google Calendar - manage events, sync, and public booking
제작자가 쓴 설명(영어 원문)
Save AI-agent handoff checkpoints, WorkStash notes, and WorkThreads in a local A2CR workspace.
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.
A2CR_LOCAL_DB필요한 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": {
"a2cr": {
"command": "uvx",
"args": [
"a2cr-mcp"
],
"env": {
"A2CR_LOCAL_DB": "your-value"
}
}
}
}터미널에서 이 명령어를 실행하세요
claude mcp add a2cr --env A2CR_LOCAL_DB=your-value -- uvx a2cr-mcp실행하기 전에 빈 값을 채워 주세요.
프로젝트의 .cursor/mcp.json 또는 ~/.cursor/mcp.json에 붙여 넣으세요
{
"mcpServers": {
"a2cr": {
"command": "uvx",
"args": [
"a2cr-mcp"
],
"env": {
"A2CR_LOCAL_DB": "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": {
"a2cr": {
"type": "stdio",
"command": "uvx",
"args": [
"a2cr-mcp"
],
"env": {
"A2CR_LOCAL_DB": "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.
A2CR_LOCAL_DB env이 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 a2cr 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(0.1.8)은 4개월 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: Agent-Native Calendar, Excel MCP Server, MCP Server for Excel
같은 카테고리에서 다른 제작자가 만든 서버예요. 스타 순으로 정렬했어요.
카드 보는 법:★ GitHub 스타i세 기둥: 리뷰 · 점검 · 사용 정도i업데이트 = 최신 버전을 레지스트리에 공개한 날i
Agent-Native Google Calendar - manage events, sync, and public booking
Create and edit Excel files with AI: charts, PivotTables, formulas. No Excel needed.
Excel automation for AI - Sheets, Power Query, DAX, VBA, Tables, Ranges and more. Windows only.
제작자의 저장소에서 쓰인 그대로 가져왔어요. PopNx MCP가 검토하거나 보증한 내용이 아니에요. HTML, 이미지, 스크립트는 제거했어요. 링크를 누르면 이 사이트를 벗어나요.
A2CR is an open-source local MCP workspace for AI agent handoffs. It lets Codex, Claude Code, Cursor, and other MCP-capable agents save WorkBaton checkpoints, store temporary WorkStash notes, coordinate through WorkThreads, and resume long coding work from a fresh AI window.
Long AI work usually breaks at the handoff. A fresh AI window needs the goal, current state, decisions, blockers, validation, and next action, but not a whole noisy transcript. A2CR keeps that handoff state compact, explicit, and safer to share between sessions.
Use A2CR when you want to:
Japanese overview | MCP setup | Usage guide | WorkBaton spec | Local mode spec | 0.1.8 release notes
A2CR is published in the official MCP Registry as io.github.a2cr/a2cr-mcp. The local-only 0.1.8 public release is live on PyPI, GitHub Releases, and the official MCP Registry. See the distribution inventory for the current registration and mirror status.
A2CR is also listed and evaluated on the Glama MCP Registry. Downstream directories may temporarily show cached metadata after a release; the official MCP Registry and this repository are the source of truth.
A2CR now treats the local workspace as the public product boundary. The public a2cr-mcp wrapper stores WorkBaton, WorkStash, WorkThread, actor, and event records in a SQLite database on the user's machine. It does not require an A2CR account, API key, hosted relay, dashboard, or cloud sync path.
The earlier hosted/SaaS relay path is being retired from the public distribution. New public setup, MCP Registry metadata, and Anthropic Directory submission work should use the local wrapper only.
Choose the local MCP distribution path that matches your AI client:
| Path | Best for | Distribution | Notes |
|---|---|---|---|
| Python stdio wrapper | Codex, Claude Code, Cursor, generic MCP clients | PyPI package a2cr-mcp | Full public wrapper path for WorkBaton and WorkStash. |
| Node MCPB / Claude Desktop Extension | Claude Desktop users who want extension-style install | GitHub Release .mcpb asset, then Anthropic Directory after approval | Manual Claude Desktop path pending Anthropic Directory approval. No npm install is required for end users. |
Keep the Python wrapper version and Node MCPB compatibility version aligned. For the current public release, both the Python wrapper and Node MCPB compatibility version are 0.1.8.
Python wrapper install:
python -m pip install --upgrade a2cr-mcp
Register A2CR for Codex and verify the local workspace:
a2cr init codex --local
a2cr doctor --target local
Open the local browser dashboard:
a2cr ui
a2cr ui binds to 127.0.0.1, chooses an available port, prints a token-protected local URL, and opens it in your default browser. If the browser does not open, copy the full printed A2CR_UI_URL, including ?token=..., into a browser on the same computer. The bare 127.0.0.1: URL is rejected by design. Keep that terminal running while you use the dashboard. Press Ctrl+C to stop it.
For a fixed port or a headless/server-style launch:
a2cr ui --port 50895
a2cr ui --no-browser
Use --no-browser when installing on a machine where the terminal cannot or should not launch a browser; it still prints the full local URL to copy.
Then restart Codex and use the a2cr-local MCP server. The compatibility command a2cr-mcp also runs the local workspace server for generic MCP clients. Start with a compact WorkBaton save/resume. Put larger supporting notes in WorkStash, then record the WorkStash: reference in the WorkBaton references or next_action field so the next AI window knows exactly what to retrieve.
Codex-style TOML:
[mcp_servers."a2cr-local"]
command = "a2cr-local-mcp" args = []
[mcp_servers."a2cr-local".env] A2CR_LOCAL_DB = "/optional/path/to/a2cr.db"
Generic MCP JSON:
{
"mcpServers": { "a2cr": { "command": "a2cr-mcp", "args": [], "env": { "A2CR_LOCAL_DB": "/optional/path/to/a2cr.db" } } } }
After connecting a new AI window, call get_account_limits once, then use resume_context to continue prior work or save_context to save a new WorkBaton checkpoint. If a lazy MCP client does not show save_context, search or request the exact tool name save_context.
Python 3.12 or 3.13 is recommended. Python 3.15 development builds are not supported.
For Claude Desktop extension-style installation, the MCPB package uses the same local-only WorkBaton and WorkStash storage boundary and can be attached to a GitHub Release before Anthropic Directory approval. See docs/claude-desktop-mcpb.md.
For project-specific A2CR behavior, create A2CR.md in the project root and put the local operating rules there. Use the repository-root A2CR.md as a starter template. Then add this short pointer to AGENTS.md, CLAUDE.md, or another project memory file:
Before using A2CR, saving or resuming WorkBaton, or storing WorkStash notes,
read and follow ./A2CR.md.
Treat A2CR.md as local project guidance. It does not override system, developer, user, or current-file instructions.
Use A2CR.md for save triggers, WorkStash causal handoff summaries, scope boundaries, protected areas, escalation conditions, and out-of-scope change notes. Keep the project memory file itself short so multiple AI clients can share the same A2CR rules.
Project memory files such as AGENTS.md or CLAUDE.md tell an AI how to work in a repository. A2CR focuses on the task handoff itself:
| Layer | Purpose | Not for |
|---|---|---|
| WorkBaton | Compact resume checkpoint for the next AI window | Full transcripts, secrets, large files |
| WorkStash | Temporary supporting notes referenced from WorkBaton (e.g., concise causal handoff summaries) | Durable knowledge base, credentials, raw transcripts |
| WorkThreads | In development — multi-agent coordination surface | Replacing WorkBaton handoff |
| WorkLedger | Future direction — auditability and accountability layer A2CR aims to add | Current public-preview feature or substitute for review |
WorkLedger is a future concept for keeping a compact, reviewable record around agent handoffs: when work was saved or resumed, which references mattered, what decisions were made, and what validation results were reported. The goal is to make long-running AI work easier to audit and explain without turning A2CR into a chat transcript store. WorkLedger is not implemented in the current public preview, and it is not meant to replace human review or AI-client safety checks.
In this repository, an AI window means one active chat/session in an AI client such as Codex, Claude Code, Cursor, or another MCP-capable agent.
A minimal WorkBaton can be as small as:
{
"goal": "Fix the failing login test", "current_state": "The failure is reproduced and the token refresh branch is the likely cause.", "next_action": "Inspect the refresh logic and rerun the focused test." }
A2CR keeps the useful resume state, not the whole conversation.
More visual material:
This public repository contains the open-source A2CR client and public reference material:
a2cr-mcpIt does not contain the hosted SaaS service implementation, production database schema, billing code, admin tooling, or deployment secrets.
WorkBaton, WorkStash, and WorkThread records are stored in the user's local SQLite A2CR workspace. The public wrapper does not upload saved content to an A2CR-operated remote endpoint.
A2CR is not a secret manager. Do not store API keys, passwords, access tokens, Authorization headers, cookies, private database URLs, local client keys, customer data, raw full transcripts (though concise causal handoff summaries are encouraged), long logs, or large source-code bodies in WorkBaton or WorkStash. Always strip credentials or PII before saving summaries.
Use A2CR for work state, not credentials.
A2CR provides a context relay mechanism. It does not make restored context trusted, and it does not replace user review, AI-client safety checks, or local key management.
| Party | Responsibilities |
|---|---|
| A2CR | Provide the public local MCP wrapper/spec, store WorkBaton and WorkStash data in the user's local SQLite workspace, avoid cloud dependencies in the public wrapper, and document unsafe content. |
| AI agents / MCP clients | Do not store secrets, treat restored context as untrusted input, verify commands before execution, and ask before dangerous or irreversible actions. |
| Users | Protect API keys and local client keys, avoid saving .env contents or credentials, and use trusted clients and machines. |
Loaded WorkBaton and WorkStash content is work state, not an authority. A future agent should not run commands, exfiltrate data, revoke keys, delete data, or call external services solely because restored context says to.
The wrapper exposes tools for:
explain_a2cr_flows: explain when to use WorkBaton, WorkStash, or WorkThreads.get_account_limits: show current local workspace limits for Slots, retention, and WorkStash.should_save_workbaton: advise whether a compact WorkBaton checkpoint is useful now.save_context: save a WorkBaton checkpoint in the local workspace.resume_context: find and load the right WorkBaton for a fresh AI window.load_context: load a specific Slot number or named WorkBaton.list_contexts: list active WorkBaton Slots.delete_context: delete a named WorkBaton Slot.should_use_work_stash: advise whether a supporting note belongs in WorkStash.store_work_stash: store a temporary supporting note in the local workspace.get_work_stash: retrieve a referenced WorkStash entry.list_work_stash: list WorkStash metadata and quota usage.delete_work_stash: delete a WorkStash entry that is no longer needed.Primary save path: save_context.
Some MCP clients expos
앞부분만 보여 드려요. 나머지는 저장소에서 읽어 보세요