프로토타입 · 미리 보기: 공식 레지스트리의 모든 서버를 담고 있으며 30분마다 동기화해요. 리뷰와 계정 기능은 다음 단계에서 열려요.
PopNx MCPAI를 쓰는 사람을 위한 MCP 서버 · 쉬운 말로 설명해요

screenpipe

제작자: screenpipe버전 0.20.4i★ –i내 컴퓨터에서 실행
레지스트리 이름io.github.screenpipe/screenpipe-mcp

설치

개요

제작자가 쓴 설명(영어 원문)

Search your local screen recordings, audio transcripts, and computer activity from screenpipe.

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: 디자인, 이미지 및 오디오

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.
  • 레지스트리에 입력해야 할 키가 나와 있지 않아요.

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": {
    "screenpipe": {
      "command": "npx",
      "args": [
        "-y",
        "screenpipe-mcp"
      ]
    }
  }
}

터미널에서 이 명령어를 실행하세요

claude mcp add screenpipe -- npx -y screenpipe-mcp

Cursor에서 열기

또는 설정을 직접 복사하기

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

{
  "mcpServers": {
    "screenpipe": {
      "command": "npx",
      "args": [
        "-y",
        "screenpipe-mcp"
      ]
    }
  }
}

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": {
    "screenpipe": {
      "type": "stdio",
      "command": "npx",
      "args": [
        "-y",
        "screenpipe-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.

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 6일 점검

  • ✓ 통과

    제작자와 저장소 일치

    레지스트리 이름이 GitHub 계정 screenpipe 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

디자인, 이미지 및 오디오 카테고리의 다른 서버와 비교i

  • 이 카테고리의 다른 서버 2164개 중 0%보다 스타가 많아요
  • 이 카테고리의 다른 서버 2164개 중 90%보다 최근에 레지스트리에 공개됐어요
  • 기본 점검 결과가 이 카테고리의 다른 서버 2164개 중 100%와 같거나 더 좋아요

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

자주 묻는 질문

screenpipe, 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?

레지스트리에 필수 키가 나와 있지 않아요. 다만 처음 쓸 때 로그인을 요구하는 서버도 있어요.

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(0.20.4)은 3일 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: Figma Context MCP, Claude Real Video, Figma MCP Server

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

Figma Context MCP

제작자: GLips

Give your coding agent access to your Figma data. Implement designs in any framework in one-shot.

내 컴퓨터에서 실행★ 16,165MIT5개월 전 업데이트
설치

Claude Real Video

제작자: HUANGCHIHHUNGLeo

Let any LLM watch a video locally — and search everything it has ever watched.

내 컴퓨터에서 실행★ 2,284MIT2개월 전 업데이트
설치

Figma MCP Server

제작자: figma

The Figma MCP server brings Figma design context directly into your AI workflow.

클라우드에서 실행★ 2,15213개월 전 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.

Screenpipe MCP Server

https://github.com/user-attachments/assets/7466a689-7703-4f0b-b3e1-b1cb9ed70cff

MCP server for screenpipe - search your screen recordings, audio transcriptions, and control your computer with AI.

Usage attribution

When Screenpipe analytics are enabled, successful nonempty retrievals produce qualified_value_event with surface (api or mcp) and agent_client. Supported app identifiers are claude, chatgpt, codex, cursor, gemini, openclaw, hermes, runner, windsurf, vscode, grok, and grokbot. Claude Desktop and Claude Code share the existing claude bucket. This identifies the caller, not the app appearing in a recording.

MCP uses SCREENPIPE_MCP_CLIENT when it names a supported app, falling back to the initialization handshake's client name. HTTP sessions resolve independently. Only the fixed identifier is forwarded; raw client names are never analytics properties. Unrecognized MCP clients remain unknown.

Installed REST skills send X-Screenpipe-Client: api and a fixed X-Screenpipe-Agent header for their target app. Other API clients can send the same headers. Missing or unrecognized app identifiers retain the legacy direct_api bucket on qualified-value events. search_performed also includes the fixed agent_client (unknown when unidentified), alongside request_source. These are self-reported identifiers, not verified app identities; copied skills and callers that omit the headers cannot be reliably attributed. Historical generic API events cannot be assigned an app retroactively.

The engine samples search_performed for every source, including app, MCP, named agents and callers without attribution headers. Each fixed source/client pair gets its first event per 60-second monotonic window per engine process, separately for empty/non-empty and parsed/ordinary results (at most four events per pair per window). The first event in each category after UTC midnight is also preserved. One client's polling cannot consume another client's budget.

API/MCP qualified_value_event emissions have an independent first-per-minute budget per surface, fixed client and value kind: search, meeting, artifact retrieved or artifact created. This covers cached API searches, activity summaries and MCP outcomes. Explicit frontend value actions are not sampled.

Both event families carry telemetry_sample_interval_seconds: 60 and telemetry_sampling: "first_per_interval_per_source_client_result_kind" for search diagnostics, or "first_per_interval_per_surface_client_value_kind" for qualified values. These are presence observations, not exact retrieval counts or unbiased samples of query properties. Counts and property breakdowns are not comparable to earlier releases. api_usage_5min.request_count still counts aggregate traffic across all routes. No search diagnostics are added for cache hits; actual searches, recording and local monitoring are unaffected.

Resource events additionally report telemetry_window_samples, window_cpu_percent_mean, window_cpu_percent_max, window_memory_gb_mean, window_memory_gb_max, and window_phys_footprint_gb_max across local samples since the previous reporting attempt. These supplement the existing point-in-time properties. Weight window means by their sample counts when aggregating them. Transport remains best effort; these summaries are not a durable sample archive.

Sampling preserves the first successful retrieval for daily presence and cohort-relative retention, not every occurrence. It reduces outgoing analytics volume after clients upgrade; actual billing savings depend on the account's pricing and usage tiers. Health/resource reporting is unchanged by retrieval sampling.

Attribution adds fields to existing events and uses the existing analytics opt-out. It does not scan running apps or send prompts, search text, or raw client names. HTTP MCP now reports successful searches through the same best-effort local telemetry endpoint as stdio MCP; empty/failed searches do not produce qualified-value events.

Call diagnostics and source references

New engine builds accept POST /internal/telemetry/mcp-call through the same local API authentication and analytics consent boundary as other private MCP telemetry. mcp_tool_call_completed records one best-effort observation per completed dispatcher call: schema version, random request ID, package version, fixed client/tool/transport names, ok/empty/error, normalized error category, duration, content bytes, optional search count/truncation and known report loss. No arguments, result text, error strings or raw client names are included. The existing qualified_value_event definition is unchanged.

ok means the dispatcher returned without a protocol/tool error. It does not mean the answer was correct or the user completed their task. Search alone sets result_count and truncated; absent fields on other tools remain unknown. response_bytes counts UTF-8 text and base64 content bytes, not total serialized JSON or model tokens. Duration includes tool execution, excluding report delivery.

The request ID is returned in _meta["screenpipe/request_id"] and forwarded to the local API as x-screenpipe-request-id. It is one call's correlation key, not a user, session or task ID. This change does not add engine log persistence for that header. Local helpers report to the engine, which applies its telemetry opt-out. SCREENPIPE_DISABLE_TELEMETRY and SCREENPIPE_TELEMETRY_DISABLED also suppress call reporting; CI suppresses it by default. Delivery is capped at eight pending requests per reporter and expires after one second. Old engines, opt-out, backend outages and process exit can lose reports. dropped_reports reports known local losses on a later delivery, not complete observation coverage.

Screen/audio/input search results preserve backend source identifiers and deep links. Both package transports cap text at 1,000 characters per hit by default; max_content_length=0 requests full text for a scoped read. The cap excludes metadata, tags and images, so callers should also limit result count. Final pages do not suggest another offset. In stdio, pass a returned frame_id to frame-context or get-frame-elements to inspect evidence beyond the snippet. HTTP clients can open the returned Screenpipe link or make a narrower search. A missing source ID stays missing.

Package capabilitystdioHTTP
Search with source references and text capssearch-contentsearch_content
Local agent chat searchsearch-chatssearch-chats
Bundled guidesscreenpipe-skillsscreenpipe-skills
Frame inspection and saved workflowsAvailableNot exposed
Enterprise team toolsWith team configurationNot exposed
Call diagnosticsBest effortBest effort, per session

This describes this package, not the separate hosted personal or enterprise MCP services. A hung backend read no longer keeps a stdio helper alive after stdin EOF: shutdown is bounded to 500 ms and affects only that client's process. This does not diagnose desktop recording health or reap another client's process.

Installable plugins

Build the local Claude Desktop extension and the cloud plugin from source:

cd packages/screenpipe-mcp

bun install --frozen-lockfile bun run build:installers

  • installers/screenpipe-local.mcpb: open in Claude Desktop's extension installer. Bundles the server and its dependencies; Claude supplies Node. Keep Screenpipe running. Includes an optional sensitive API key setting and API URL setting.
  • installers/screenpipe-cloud.zip: portable remote plugin with OpenAI/Codex and Claude compatibility manifests. Requires Data Sync and OAuth sign-in; exposes only synced device listing and search. See the cloud setup guide.
  • installers/SUBMISSION.md: listing copy, endpoint, permissions guidance, and acceptance cases for directory submission. Building does not publish a listing.

The archive builder uses an explicit file allowlist, so local environment files, credentials, development dependencies, and source tests are not packaged.

Installation

Option 1: The screenpipe desktop app (Recommended)

The most reliable setup is to install the screenpipe desktop app and connect Claude Desktop from Settings → Connections (or during onboarding). This writes a config that:

  • uses the bundled `bun` shipped with the app (an absolute path — no Node/npx or PATH dependency, and ~3× faster cold start), and
  • injects your `SCREENPIPE_LOCAL_API_KEY` into the server's env, so the MCP authenticates instantly instead of running slow key discovery at startup.

Both matter: a config without the key forces the server to discover it via subprocess fallbacks, which on a cold package cache can stall Claude Desktop's MCP startup and produce Could not attach to MCP server screenpipe.

Option 2: Manual NPX (no desktop app)

If you're not using the desktop app, edit your Claude Desktop config:

  • macOS: ~/Library/Application Support/Claude/claude_desktop_config.json
  • Windows: %AppData%\Claude\claude_desktop_config.json
{

"mcpServers": { "screenpipe": { "command": "npx", "args": ["-y", "screenpipe-mcp@latest"], "env": { "SCREENPIPE_LOCAL_API_KEY": "sp-…" } } } }

Requires Node/npx on PATH. Pin @latest so the first install doesn't cache a stale version forever. Get your key with screenpipe auth token. If you omit the key, the server will try to discover it through the bundled Screenpipe CLI and then npx. It never opens the live SQLite database directly. Discovery can be slower on first run, so setting the environment variable is still recommended.

Enterprise admins: add SCREENPIPE_ENTERPRISE_TOKEN (and, for orgs running their own query gateway, SCREENPIPE_TEAM_API_URL) to the same env block to get the team-* tools — see Enterprise team tools.

Option 3: HTTP Server (Remote / Network Access)

The MCP server can run over HTTP using the Streamable HTTP transport, allowing remote MCP clients to connect over the network instead of stdio. This is ideal when your AI assistant (e.g., OpenClaw) runs on a different machine than screenpipe.

# loopback only (default)

npx -y screenpipe-mcp --http --port 3031

expose to your LAN with bearer auth

npx -y screenpipe-mcp --http --listen-on-lan --api-key $(openssl rand -hex 16)

or from source — must build first so dist/ exists

bun install && bun run build bun run start:http -- --port 3031

Tip: npx screenpipe-mcp-http (without --http) does not work —

npm resolves by package name, and there is no screenpipe-mcp-http

package. The HTTP server ships as a transport inside the

screenpipe-mcp package; use --http as shown above, or invoke the

bin directly with npx -p screenpipe-mcp screenpipe-mcp-http.

The server exposes:

  • MCP endpoint: http://localhost:3031/mcp — Streamable HTTP transport (POST for requests, GET for SSE stream)
  • Health check: http://localhost:3031/health — always unauthenticated, for monitors

Options:

FlagDescriptionDefault
--portPort for the MCP HTTP server3031
--screenpipe-portPort where screenpipe API is running3030
--listen-on-lanBind 0.0.0.0 so other devices on the LAN can connect. Requires --api-key.off (binds 127.0.0.1)

| --api-key | Bearer token required for non-loopback requests (`A

앞부분만 보여 드려요. 나머지는 저장소에서 읽어 보세요

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