Agent-Native Calendar
Agent-Native Google Calendar - manage events, sync, and public booking
제작자가 쓴 설명(영어 원문)
Shared, source-backed memory for AI agents on your own Postgres, with an optional document engine.
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": {
"myco-brain": {
"command": "npx",
"args": [
"-y",
"@mycobrain/mcp-server"
]
}
}
}터미널에서 이 명령어를 실행하세요
claude mcp add myco-brain -- npx -y @mycobrain/mcp-server프로젝트의 .cursor/mcp.json 또는 ~/.cursor/mcp.json에 붙여 넣으세요
{
"mcpServers": {
"myco-brain": {
"command": "npx",
"args": [
"-y",
"@mycobrain/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": {
"myco-brain": {
"type": "stdio",
"command": "npx",
"args": [
"-y",
"@mycobrain/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 8일 점검
레지스트리 이름이 GitHub 계정 thegoodguysla 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(1.3.0)은 24일 전 레지스트리에 올라왔어요.
Claude Desktop, Claude Code, Cursor, VS Code처럼 MCP를 지원하는 앱에서 쓸 수 있어요. 내 컴퓨터에 Node.js도 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:↓ 주간 다운로드i★ 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.
Shared memory for your AI agents, on your own Postgres. Every fact keeps its source, a change supersedes the old fact instead of overwriting it, and each client's facts stay inside that client's boundary.
npm install -g @mycobrain/mcp-server
mycobrain-setup memory
You need Node.js 20 or later. Setup asks a few questions, shows each file it is about to change, and waits for a yes. It backs up every file it edits, writes no secret into any of them, and finishes by writing one fact and reading it back. mycobrain-setup memory --uninstall removes what it added.
custom, a short interview that writes a template for your own business. A template sets how clients are split into houses, the kinds of entities, and what is worth remembering.brain_memory_context and brain_memory_write, and instructions that tell the agent to recall before it acts and to record what it learns.mycobrain-memory history shows both and the reason.mycobrain-memory binding add --house gives a contractor's agent one client and nothing else. Each tool gets its own credential, so you can see which tool wrote a fact and revoke one tool without touching the others.claude or codex CLI you are already signed in to. Embeddings run locally with Ollama if you have it. Without Ollama, recall matches the words of your prompt, which works well for names, places and numbers.mycobrain-memory review approve.Check it at any time with mycobrain-doctor --memory. Walkthrough: docs/memory-quickstart.md. Every command: docs/memory-cli.md.
Before release we ran the documented commands from the packed npm tarball in a fresh home directory, set up as a fictional landscape firm. A decision made in the first session came back unprompted in the third. A change superseded the old fact and kept its history. An agent scoped to one client found nothing from another. With the database stopped, sessions still started and a write was queued and delivered later. Uninstall put the config files back byte for byte.
The engine is the part of Myco Brain that came first: ingest documents, repos and chat exports, search them, build a knowledge graph, and trace every answer to the document it came from. It runs as a Docker stack, next to the memory layer or on its own. Add it when you want answers from your documents, not only the facts your agents record. Most of what follows is about the engine.
brain_why) — no trust-me summaries.Who it's for: dev teams running agents that need one shared memory · agencies needing hard per-client isolation · anyone who wants their assistant to remember across sessions — import your ChatGPT / Claude history (from your data export) and your AI knows you on day one.
The usual fix for agent amnesia — letting an LLM maintain its own memory — fills it with duplicates, hallucinated summaries, and confident answers nobody can trace. Myco Brain is built on the opposite contract:
**The LLM proposes. Deterministic rules decide what becomes a fact. You set
the bar** — from corroboration-gated auto-promotion (the default) to strict
human review of every fact (
BRAIN_REQUIRE_HUMAN_REVIEW=1).
Claude, Cursor, Windsurf, Continue, Zed, and custom agents all share one memory backed by your own Postgres.
⭐ If the trust model resonates, a star helps others find it.
# 1. Boot the stack (Postgres + MCP server + extraction worker)
git clone https://github.com/thegoodguysla/myco-brain.git && cd myco-brain docker compose up -d
npx -y -p @mycobrain/mcp-server mycobrain-ingest github:your-org/your-repo
[!TIP]
Zero API keys, all the way down. Full-text search, semantic search (local
embeddings), and the knowledge graph (local extraction) all run with no hosted
dependency. Add an Anthropic key only if you want the most accurate graph.
MCP-native by design — your agent knows _when_ to use memory, not just _how_. Most MCP servers expose tools and hope the model calls them. Myco ships a usage contract over MCP's instructions channel: the moment it connects, your agent knows to pull context before a task, save durable decisions, and cite sources with brain_why — no per-project prompting. Tune the policy in one copy-paste block: Teach your agent to use it well.
Quick links: Memory in two commands · Engine quickstart · Teach your agent to use it well · The trust engine · Benchmark — run it yourself · Run every proof · Who it's for · Environment variables · Architecture · Roadmap · Cloud waitlist
Most agent memory overwrites facts silently. Myco Brain compounds them:
brain_why about any fact and you get its distinct source count (per relationship, not per mention), its confidence trend over time ("0.8 → 0.86"), and any superseded history. Contradictions stay visible. Your memory can't gaslight you.works for → Halcyon Labs 0.55 [SUPERSEDED — kept, not deleted]
works for → Driftwood Analytics 0.90 [ACTIVE] claims ledger: old fact superseded_by → new fact (audited)
*Proof: npm run test:compounding — the full lifecycle runs against a live database in seconds, no LLM required.*
brain_stats: *"Brain proposed 3 new types from your data"*).BRAIN_SCHEMA_AUTO_PROMOTE=1), counted per document, not per mention, so two documents passed back and forth can't manufacture consensus. One chatty document can never promote anything.*Proofs: npm run test:dynamic-schema, npm run test:schema-promotion.*
You pick the trust dial:
| Mode | Behavior |
|---|---|
| Default | Confident facts auto-promote; novel types wait for review |
BRAIN_REQUIRE_HUMAN_REVIEW=1 | Strict curation — nothing the LLM proposes touches the canonical graph without a human decision |
BRAIN_SCHEMA_AUTO_PROMOTE=1 | Corroborated new types promote themselves, audited |
When something is waiting on you — novel types in default mode, or everything in strict mode — review it from the command line:
mycobrain review # list pending entities, relationships, types
mycobrain review approve # promote it into the graph mycobrain review reject # reject it (kept and audited, never deleted)
*Proof: npm run test:review — approving actually lands the entity / edge / type in the canonical graph; rejecting never does.*
Multi-agent teams get real isolation: documents marked private are readable only by the agent that created them — enforced in every read tool, on top of workspace row-level security. Workspace memory stays shared. *Proof: npm run test:sharing (a two-agent visibility matrix).* Like workspace isolation, it binds only under the least-privilege brain_app role (security note).
Put each client in their own workspace, share one agency-wide playbook, and the guarantee you sell is Postgres row-level security — a session scoped to Client A cannot return Client B's rows. The agency starter kit provisions it (one command) and ships the least-privilege DB role that makes the isolation actually bind. *Proof: npm run test:agency — Client A sees zero of Client B's facts.*
[!IMPORTANT]
**Isolation binds only under the least-privilege rol
앞부분만 보여 드려요. 나머지는 저장소에서 읽어 보세요