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AtlaSent MCP Server

제작자: AtlaSent버전 2.21.0i★ –i내 컴퓨터에서 실행
레지스트리 이름io.github.AtlaSent/mcp-server

설치

개요

제작자가 쓴 설명(영어 원문)

AtlaSent for AI agents: with an API key, actions wait for approval with signed proof. No key: demo.

PopNx MCP의 세 기둥

이 세 가지는 절대 하나의 점수로 합치지 않아요. 의견, 측정, 인기는 서로 다른 것이니까요.

할 수 있는 일

이 서버는 아직 쉬운 말로 된 요약을 쓰지 못했어요. 아래는 레지스트리에서 읽어 온 사실이에요.

  • 연결 방식: 내 컴퓨터에서 실행
  • 전송 방식: stdio
  • 카테고리: 기타
  • 입력해야 할 값: ATLASENT_API_KEY

설치

쓰고 있는 AI 앱을 고른 다음 설정을 복사하세요.

설치하기 전에

  • 기본 점검: 1개 중 1개 통과
  • 내 컴퓨터에서 내 사용자 계정의 권한(파일, 네트워크, 앱)으로 실행돼요.
  • 직접 입력할 값: ATLASENT_API_KEY, ATLASENT_BASE_URL, ATLASENT_MODE

비밀로 표시된 값은 내 계정의 열쇠예요. 절대 다른 사람과 공유하거나 채팅에 붙여 넣지 마세요.

필요한 것

  • npmNode.js 설치(npx 명령어용)

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": {
    "atlasent": {
      "command": "npx",
      "args": [
        "-y",
        "@atlasent/mcp-server"
      ],
      "env": {
        "ATLASENT_API_KEY": "your-value",
        "ATLASENT_BASE_URL": "your-value",
        "ATLASENT_MODE": "your-value"
      }
    }
  }
}

터미널에서 이 명령어를 실행하세요

claude mcp add atlasent --env ATLASENT_API_KEY=your-value --env ATLASENT_BASE_URL=your-value --env ATLASENT_MODE=your-value -- npx -y @atlasent/mcp-server

실행하기 전에 빈 값을 채워 주세요.

Cursor에서 열기

또는 설정을 직접 복사하기

프로젝트의 .cursor/mcp.json 또는 ~/.cursor/mcp.json에 붙여 넣으세요

{
  "mcpServers": {
    "atlasent": {
      "command": "npx",
      "args": [
        "-y",
        "@atlasent/mcp-server"
      ],
      "env": {
        "ATLASENT_API_KEY": "your-value",
        "ATLASENT_BASE_URL": "your-value",
        "ATLASENT_MODE": "your-value"
      }
    }
  }
}

브라우저가 앱을 열지 물어본 다음, 앱이 설정을 보여 주고 확인을 요청해요. 키 값은 비워 두니 직접 입력하세요.

VS Code에서 열기

또는 설정을 직접 복사하기

프로젝트의 .vscode/mcp.json에 붙여 넣으세요

{
  "servers": {
    "atlasent": {
      "type": "stdio",
      "command": "npx",
      "args": [
        "-y",
        "@atlasent/mcp-server"
      ],
      "env": {
        "ATLASENT_API_KEY": "your-value",
        "ATLASENT_BASE_URL": "your-value",
        "ATLASENT_MODE": "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.

직접 입력할 값

  • ATLASENT_API_KEY 비밀 env
    AtlaSent API key: sign up free at console.atlasent.io and choose Connect an AI agent. Leave blank for the local demo mode.
  • ATLASENT_BASE_URL env
    AtlaSent API base URL. Defaults to the hosted SaaS endpoint.
  • ATLASENT_MODE env
    remote (default when an API key is set) or local (in-process rules engine, no API calls)

이 설정은 제작자의 레지스트리 정보로 만들었어요. README와 다르면 README를 따르세요.

할 수 있는 일

내 컴퓨터에서 실행돼요. 설치하면 AI 앱에 도구 목록이 나타나요.

기본 점검i

누구나 볼 수 있는 정보를 점검했고, 코드는 읽지 않았어요. 해당하지 않는 항목은 개수에서 뺐어요. · 2026년 11월 7일 점검

  • ✓ 통과

    제작자와 저장소 일치

    레지스트리 이름이 GitHub 계정 AtlaSent 기준으로 확인되었고, 이 계정이 저장소를 소유하고 있어요

  • … 아직 점검 안 함

    계속 관리되고 있음

    아직 GitHub에서 이 저장소를 읽어 오지 않았어요

  • … 아직 점검 안 함

    라이선스 있음

    아직 GitHub에서 이 저장소를 읽어 오지 않았어요

  • – 해당 없음

    엔드포인트 응답

    내 컴퓨터에서 실행되는 서버라 인터넷 엔드포인트가 없어요

  • – 해당 없음

    OAuth로 로그인

    내 컴퓨터에서 실행되며, 대신 로컬 설정을 사용해요

  • … 아직 점검 안 함

    도구 설명에 숨겨진 지시 없음

    내 컴퓨터에서 실행돼요. 도구 목록은 인터넷 엔드포인트가 있는 서버에서만 읽을 수 있어요

기타 카테고리의 다른 서버와 비교i

  • 이 카테고리의 다른 서버 32589개 중 0%보다 스타가 많아요
  • 이 카테고리의 다른 서버 32589개 중 76%보다 최근에 레지스트리에 공개됐어요
  • 기본 점검 결과가 이 카테고리의 다른 서버 32589개 중 100%와 같거나 더 좋아요

레지스트리에서 이 카테고리의 활성 서버 전체와 비교했어요. 스타는 게시자가 저장소를 소유한 경우에만 세요.

자주 묻는 질문

AtlaSent MCP Server, 안전한가요?

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.

어떤 키나 코드가 필요한가요?

입력해야 할 값: ATLASENT_API_KEY

내 컴퓨터에서 실행되나요, 클라우드에서 실행되나요?

내 컴퓨터에서 실행돼요. AI 앱이 필요할 때 실행하고, 데이터는 제작자의 서버를 거치지 않아요(서버 자체가 온라인 서비스를 호출하는 경우는 제외).

아직 관리되고 있나요?

GitHub 저장소는 마지막으로 – 변경됐고, 최신 버전(2.21.0)은 11일 전 레지스트리에 올라왔어요.

어떤 앱에서 쓸 수 있나요?

Claude Desktop, Claude Code, Cursor, VS Code처럼 MCP를 지원하는 앱에서 쓸 수 있어요. 내 컴퓨터에 Node.js도 있어야 해요.

대신 쓸 수 있는 건 뭐가 있나요?

같은 카테고리에는: Spec Driven Development, Remote Desktop Commander, Detailing Manager

대안

같은 카테고리에서 다른 제작자가 만든 서버예요. 스타 순으로 정렬했어요.

카드 보는 법:★ GitHub 스타i세 기둥: 리뷰 · 점검 · 사용 정도i업데이트 = 최신 버전을 레지스트리에 공개한 날i

Spec Driven Development

제작자: formulahendry

MCP Server that facilitates spec-driven development workflows, not just Vibe Coding.

내 컴퓨터에서 실행★ 440MIT14개월 전 업데이트
설치

Remote Desktop Commander

제작자: desktop-commander

Remote Desktop Commander: use files, terminal and processes on your own computers from any AI app.

클라우드에서 실행★ 211어제 업데이트
설치

Detailing Manager

제작자: RadoslavMoshev

Connect your auto detailing studio's tasks, cars, clients and revenue to Claude or ChatGPT.

내 컴퓨터에서 실행오늘 업데이트
설치

제작자의 README

README 열기

제작자의 저장소에서 쓰인 그대로 가져왔어요. PopNx MCP가 검토하거나 보증한 내용이 아니에요. HTML, 이미지, 스크립트는 제거했어요. 링크를 누르면 이 사이트를 벗어나요.

@atlasent/mcp-server

MCP server that enforces authorize-before-execute for any MCP-compatible AI agent.

It is the developer entry point to AtlaSent AI Action Protection, which Enterprise teams adopt through a design partner program.

AtlaSent is security and organizational authority infrastructure for consequential actions by people, software, and AI.

This MCP server brings it to AI agents (Claude, Cursor, Windsurf, any MCP host). Before an agent's tool call changes a real system (a production deploy, a data export, an access grant), the agent asks AtlaSent first. With an API key (remote mode):

  1. Connect it to your agent with a few lines of config.
  2. Risky actions wait for a person to approve them. Everything else runs as normal.
  3. Every decision gets a signed record, including who approved it when the decision relied on an approval.

Try it in 60 seconds with no account: npx -y @atlasent/mcp-server (local mode, a demo that protects nothing).

Free, no account: paste your MCP server's tool list into AI Permission Check to see which tools could change real systems and who should approve them. It runs in your browser.

Local blocking is free. To actually block on your own machine without an account, use MCP Gate or the Claude Code agent guard. Both are part of the free Community plan. Hosted decisions, shared policies and approvals for a team are on the AtlaSent plans.

For engineers

AtlaSent performs execution-time authorization: determine whether a specific consequential Action is authorized now, issue a bounded Permit on allow, verify that Permit at the execution Gate, and only then allow the governed native effect.

A plausible request is not organizational authority.

This MCP server exposes AtlaSent authorization primitives to Model Context Protocol hosts and includes a protected deployment demo that proves the ordering end to end.

Which authority decided?

This repository ships two packages, and one of them has two modes. All three block tool calls. Only one of the three is evidence, and the difference is not a feature list — it is *who said yes*.

SurfaceWho decidedWhat it is
@atlasent/mcp-server local modea built-in heuristicnothing — a credential-free demo. Its terminal rule is allow, including for action types it does not recognise. Never rely on it as protection.
@atlasent/mcp-gate + policy.jsonyou, in advance, in a file you can editoperator configuration. Starts at {"default":"deny","rules":[]} and blocks everything until you write a rule. Runs with no account and no network.
@atlasent/mcp-gate cloud modeyour organization, at execution timean organizational permit — single-use, bound to that call, verifiable afterwards.

A rule you can silently edit is configuration. A permit your organization issued, that was consumed once and can be produced later, is authority. Both stop the call; only the second answers *"who authorized this?"* — which is the question that arrives after an incident, not before one.

The gate says which one decided, on every decision: no_matching_rule is your local policy, cloud_permit_consumed is an organizational permit. These reason strings are deliberately not normalised into a generic "blocked." Do not collapse them.

The two packages point in opposite directions, which is why they are separate: mcp-server exposes AtlaSent *as* MCP tools an agent calls to ask for authorization; mcp-gate sits *in front of* someone else's MCP server and intercepts.

The invariant

For an enforced protected path:

Action proposed

→ current organizational Authority + Policy + Context evaluated → Decision deny / hold / escalate → STOP allow → bounded Permit → Permit Verification at the execution Gate invalid / expired / replayed / mismatched / error → STOP verified → native effect may execute → execution/native-effect Evidence recorded where the integration supplies it

Evaluation is not execution. A positive Decision is not the Gate. Permit Verification happens before the protected side effect.

Quickstart: 60 seconds, no account

You don't need an AtlaSent account or API key to try this server. With no credentials set, it runs in local mode: an in-process rules engine that works offline.

One-click install (local mode, no account):

Add this to your MCP host config (Claude Desktop, Claude Code, Cursor, Windsurf, VS Code, and others; per-host file locations are below):

{

"mcpServers": { "atlasent": { "command": "npx", "args": ["-y", "@atlasent/mcp-server"], "env": { "ATLASENT_MODE": "local" } } } }

Then ask your agent to *"deploy billing-api to production"*. The built-in rules deny it because it has no approvals. Ask again with an approval and it's allowed, and the server verifies the permit before the simulated deploy runs.

Built-in local rules (src/localEngine.ts):

SituationDecision
Production action with no approvalsdeny
Destructive action (delete, drop, purge, ...) outside a change windowhold
Sign / certify / grant / revoke / suspend / resume actionsdeny
Override / release / export / import / publish actionshold
Anything that passes the rulesallow → single-use permit, 5-minute TTL

Local permits are unsigned, so local mode is for development, CI, and trying things out. It's not a production enforcement boundary. The server refuses to fall back to local mode under NODE_ENV=production. When you're ready for signed permits, audit evidence, and your organization's own policies, switch to remote mode — get an API key.

Run from source
git clone https://github.com/AtlaSent/atlasent-mcp-server.git

cd atlasent-mcp-server npm install npm run build npm run demo # blocked deploy → approved + verified deploy → replay refused, fully offline

Run with Docker
docker build -t atlasent-mcp .

docker run -i --rm atlasent-mcp # local mode, stdio docker run -i --rm -e ATLASENT_API_KEY -e ATLASENT_BASE_URL atlasent-mcp # remote mode

Claude Code plugin: stop agents destroying production

packages/agent-hooks is a Claude Code plugin. Destructive and shipping commands wait for a person: DROP TABLE, terraform destroy, volume deletes, git push --force, production deploys. With nobody to ask, they are refused. Everything else runs as normal. Local, no account.

/plugin marketplace add AtlaSent/atlasent-mcp-server

/plugin install atlasent-guard@atlasent

"Someone at the keyboard said yes" is where it stops. The rest of this repository is the organizational version: an approver your organization named, and a permit you can prove afterwards.

Canon-backed Actions

AtlaSent does not treat every ad-hoc tool string as a new governed Action Type.

Use the Protected Action Canon for stable Action identity. Two important examples are:

production.deploy

agent.tool.invoke

For a generic AI tool invocation, use agent.tool.invoke as the public Canon-backed Action Type and carry tool-specific facts—tool name, target, environment, arguments/payload digest, resource state, and other required context—in the authorization context or binding fields supported by the selected integration path.

Use the read-only atlasent_lookup_action tool to discover Canon-backed Action Types instead of inventing a parallel taxonomy.

Authority is not Approval

Keep the concepts separate:

  • Authority — standing, scoped organizational right to cause a class of change.
  • Authorization — per-request determination whether this exact Action may proceed now.
  • Policy — versioned conditions applied to the determination.
  • Approval — verified input that may satisfy a Policy condition; not standing Authority and not the final Authorization result.
  • Decision — allow | deny | hold | escalate at the platform boundary.
  • Permit — bounded positive-Authorization artifact.
  • Verification — execution-boundary check of the Permit and applicable bindings.
  • Execution / native effect — what the underlying tool or system actually does.
  • Evidence / Proof — durable evidence of the authorization and, where observed, the effect/result.

A human Approval, favorable risk signal, policy match, deployment ticket, or workflow status does not by itself become organizational Authority.

Protected-tool demo

deploy_service is intentionally small. It demonstrates a two-layer protected path:

agent requests deploy_service

→ authorize internal agent-tool compatibility gate → verify outer Permit → authorize production.deploy → verify production.deploy Permit → simulated deployment effect

The internal outer gate uses the Canon-backed agent.tool.invoke Action (CANON-000026 / ACT-0029) — the same public identifier documented throughout the AtlaSent ecosystem as the canonical generic AI-agent tool invocation. It previously used a legacy, uncatalogued identity, model.agent.execute_tool, which had no corresponding action_classes provisioning path in the runtime (no seed/migration anywhere creates a row with that slug) — so against a real, unmodified Atlasent org the outer gate could only ever return NO_ACTION_CLASS deny, regardless of the tool-specific inner gate's own decision. Migrating the outer gate onto agent.tool.invoke gives it the runtime's real "AI Agent Safeguard" provisioning path, which already exists for exactly this purpose. See AtlaSent/atlasent-mcp-server#121 for the full investigation and decision record.

If either Decision is non-allow or either Permit fails Verification, no deployment result is produced.

The protected-tool response includes the action-specific Verification result alongside the simulated native result:

{

"decision": "allow", "permit_token": "...", "verification": { "outcome": "verified", "valid": true }, "result": { "status": "deployed", "service": "billing-api" } }

The returned Permit has already been consumed by the execution-boundary Verification. Verifying it again should be treated as a replay, not as a step required after deployment.

Human-in-the-loop demo: hold, approve, execute, record

`examples/hitl-demo.mjs

앞부분만 보여 드려요. 나머지는 저장소에서 읽어 보세요

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