Screenpipe MCP Server

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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.

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.

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:

When the Screenpipe API requires authentication, set SCREENPIPE_LOCAL_API_KEY in the HTTP server's environment (SCREENPIPE_API_KEY is the fallback). Obtain it with screenpipe auth token, and keep it out of client configuration and chat. The server uses this key for upstream API requests, including retrieval telemetry. --api-key is a separate credential for callers connecting to MCP; it is never forwarded to the Screenpipe API.

SCREENPIPE_LOCAL_API_KEY="$(screenpipe auth token)" npx -y screenpipe-mcp --http

Connecting a remote MCP client:

Point any MCP client that supports HTTP transport at the /mcp endpoint:

{
  "mcpServers": {
    "screenpipe": {
      "url": "http://<your-ip>:3031/mcp",
      "headers": {
        "Authorization": "Bearer <your-secret>"
      }
    }
  }
}

If your machines are on different networks, expose port 3031 via Tailscale, SSH tunnel, or similar — see the OpenClaw integration guide for detailed examples.

Note: The HTTP server currently exposes search_content only. The stdio server has the full tool set (export-video, list-meetings, activity-summary, search-elements, frame-context). We're working on bringing HTTP to full parity.

Option 4: From Source

Clone and build from source:

git clone https://github.com/screenpipe/screenpipe
cd screenpipe/packages/screenpipe-mcp
bun install
bun run build

Then configure Claude Desktop:

{
  "mcpServers": {
    "screenpipe": {
      "command": "node",
      "args": ["/absolute/path/to/screenpipe-mcp/dist/index.js"]
    }
  }
}

Note: Restart Claude Desktop after making changes.

Enterprise team tools (team-*)

team-search, team-devices, team-records, and team-frame query your whole org instead of just this machine. They are registered only when an enterprise admin token is present, and they need two independent settings: a token, and the base URL of the API that token is valid for.

1. The token

An sk_ent_… enterprise admin token, resolved in this order:

1. SCREENPIPE_ENTERPRISE_TOKEN env var (MCP config, terminal)
2. team_api_token in ~/.screenpipe/enterprise.json — written by the desktop app's Settings → Privacy → Admin Team API Token

No token → the team-* tools do not appear at all.

The MCP rereads enterprise.json for each team request, so replacing or clearing the saved token takes effect without restarting it. An explicit SCREENPIPE_ENTERPRISE_TOKEN still takes priority. If that override expires, replace it or remove it to use the desktop app's saved token, then restart the MCP server to reload its environment.

2. The base URL

Trailing slashes are trimmed, so both …/v1 and …/v1/ work.

If your org runs its own query gateway, you must set this. On the write-only archive tier your telemetry never reaches the hosted API — it lands in your own object storage and is served by a gateway inside your network. The hosted API has no read path to that data, so leaving the default in place makes every team-* call fail with HTTP 401, even though the tools show up and the token is valid. The bearer token is the same either way; only the base URL moves.

Full Claude Desktop config for a gateway org:

{
  "mcpServers": {
    "screenpipe": {
      "command": "npx",
      "args": ["-y", "screenpipe-mcp@latest"],
      "env": {
        "SCREENPIPE_LOCAL_API_KEY": "sp-…",
        "SCREENPIPE_ENTERPRISE_TOKEN": "sk_ent_…",
        "SCREENPIPE_TEAM_API_URL": "https://sp-gw.acme.internal/api/enterprise/v1"
      }
    }
  }
}

Verify which base a running server picked up by asking for team-devices — the error text on a missing token also prints the base currently in use.

Known limits

• Requires screenpipe-mcp >= 0.19.0. 0.18.15 and earlier hardcode the hosted base and cannot be repointed at a gateway at all. Check with npm view screenpipe-mcp version, or read serverInfo.version from the MCP initialize response.
• Priority 3 is populated by the desktop app, which writes gateway_url from the org's policy poll. It needs an admin signed in on that machine; on a server or CI box use the env var or the flag.
• The HTTP transport (--http) exposes search_content only; team-* tools are stdio-only.
• The .mcpb bundle (Claude Desktop extension install) has no UI for these variables — use the manual NPX config above for gateway orgs.

Testing

Test with MCP Inspector:

npx @modelcontextprotocol/inspector npx screenpipe-mcp

Transport Modes

Available Tools

search-content

Search through recorded content with content type filtering:

• all — OCR + Audio + Accessibility (default)
• ocr — Screen text from screenshots
• audio — Audio transcriptions
• input — User actions (clicks, keystrokes, clipboard, app switches)
• accessibility — Accessibility tree text
• parsed — Compact app-specific messages, emails, tasks, documents, and code review (experimental; may be empty when parsing is disabled or unsupported)
• Time range, app/window, and speaker filtering
• Parsed data can also be filtered by frame_id or resolved actor_id
• Pagination support

export-video

Export screen recordings as video files:

• Specify time range with start/end times
• Configurable FPS for output video

activity-summary

Get a lightweight compressed activity overview for a time range:

• Authoritative total active minutes and server data status
• Per-app active minutes and a bounded window/tab breakdown
• Edited document paths plus an optional bounded parsed-context sample for identifying tasks
• Recent accessibility text and audio context

Use active-minute fields for duration. Edited paths, parsed rows, frames, text, and audio samples are supporting context only; their counts are not time. Parsed capture is experimental and may be disabled or unsupported; the base activity summary remains complete without it.

list-meetings

List detected meetings with id, duration, app, attendees, and note snippet. Pass q to filter by substring (title, attendees, notes) — q searches all meeting history, so omit the time range when looking for a person or topic. Follow up with get-meeting (optionally include_transcript: true) for the full note and speaker-attributed transcript.

search-elements

Search structured UI elements (accessibility tree nodes and OCR text blocks):

• Filter by source, role, app, time range
• Much lighter than search-content for targeted UI lookups
• Returns a compact outline view by default — a deduped, indented tree of the text-bearing nodes (#id refs, (off-screen) flags), ~91% fewer tokens than raw element JSON
• Pass purpose: "automation" for automation targeting context with response-local refs, best-effort stable keys, state, bounds, and allowed actions. Refresh it before each action; historical database ids are not live control handles. computer-use remains a legacy alias.
• If purpose is omitted, the MCP follows the desktop AI context use setting. The backward-compatible default remains the read/memory outline.

get-frame-elements

The whole element tree for one frame, as the same compact outline. Pass purpose: "automation" for the automation targeting view.

frame-context

Get accessibility text, parsed tree nodes, and extracted URLs for a specific frame.

keyword-search

Fast FTS5 keyword search across OCR + audio combined. Returns matches with frame_id, app, timestamp, and text positions.

list-meetings / get-meeting / update-meeting / start-meeting / stop-meeting

Manage the meeting store. list-meetings filters by substring; get-meeting returns title/attendees/times/full note (add include_transcript: true for the speaker-attributed transcript). update-meeting writes only the fields you pass. start-meeting and stop-meeting drive manual meeting recording sessions.

search-speakers / list-unnamed-speakers / update-speaker / merge-speakers

Speaker identification workflow. Search by name prefix, list speakers that haven't been named yet, rename a speaker, or merge two speakers when the same person was detected as different ones.

add-tags

Tag a screen frame (vision) or audio chunk (audio) so it can be retrieved later.

update-memory

Create, update, or delete a persistent memory (facts, preferences, decisions the user wants to remember).

send-notification

Send a notification to the screenpipe desktop UI.

control-recording

Start or stop audio recording. This does not pause or resume screen capture.

health-check

Check if screenpipe is running and healthy. Returns recording status, frame/audio stats, and timestamps.

list-audio-devices

List available audio input/output devices for recording.

list-monitors

List available monitors/screens for capture.

list-pipes / create-pipe / run-pipe / pipe-logs

Manage pipes — scheduled AI automations that run a markdown prompt on a schedule (e.g. "every day at 9am"). list-pipes shows enabled state + schedule; create-pipe creates one; run-pipe triggers a one-off test run; pipe-logs fetches recent execution output.

team-search / team-devices / team-records / team-frame

Team-tier tools, registered only when an enterprise admin token is configured. team-search runs substring search across the entire org's telemetry, team-devices lists enrolled devices (hostname, OS), team-records dumps chronological frame, parsed-app, or audio data for a time window, and team-frame returns one bounded PII-redacted JPEG using IDs from search or records. Orgs running their own query gateway must also set SCREENPIPE_TEAM_API_URL — see Enterprise team tools for the full precedence order.

Example Queries in Claude

• "Search for any mentions of 'rust' in my screen recordings"
• "Find audio transcriptions from the last hour"
• "Show me what was on my screen in VSCode yesterday"
• "Export a video of my screen from 2-3pm today"
• "Find what John said in our meeting about the database"
• "What did I type in Slack today?" (uses content_type=input)
• "What did I copy to clipboard recently?" (uses content_type=input)
• "Show me accessibility text from Chrome" (uses content_type=accessibility)

Requirements

• screenpipe must be running on localhost:3030
• Node.js >= 18.0.0

Notes

• All timestamps are handled in UTC
• Results are formatted for readability in Claude's interface
• macOS automation features require accessibility permissions
• The MCP tools already return compact, readable text (the element tools follow the desktop capture profile unless the caller sets a purpose). If you instead call the underlying screenpipe REST API directly (e.g. via curl), the list endpoints (/search, /elements, /frames/{id}/elements) accept ?format=csv|tsv for a columnar table (column names written once) and ?fields=a,b,c to select only the columns you need (dotted paths like content.text); the element endpoints also accept ?format=outline (~91% fewer tokens than JSON) or ?format=automation for fresh refs, best-effort keys, actions, state, and bounds. On list-shaped results that is a 70–91% token cut versus the default JSON, which stays unchanged when no param is set.

Privacy Policy

The Screenpipe MCP server is a local-only bridge between Claude and your local Screenpipe instance. It does not collect, transmit, or store tool results, recordings, OCR text, audio transcripts, screenshots, or UI events on its own.

What this MCP server does

When Claude invokes a tool (search-content, activity-summary, etc.) the MCP server forwards the request to http://localhost:3030 — the Screenpipe daemon running on your machine — and returns the response. That's the entire data path.

Data collection

The MCP server sends privacy-preserving crash and error reports to Screenpipe's Sentry project so we can diagnose startup failures like "server disconnected" or "could not attach to MCP server". These reports include the MCP package version, runtime, transport mode, and sanitized exception details. They do not include tool arguments, tool results, screen content, audio, transcripts, screenshots, API tokens, or your home-directory path.

To disable crash/error reporting, set any of: SCREENPIPE_MCP_SENTRY_DISABLED=1, SCREENPIPE_TELEMETRY_DISABLED=1, or SCREENPIPE_DISABLE_TELEMETRY=1 in the MCP launch environment.

Data usage

Tool calls are passed straight through to your local Screenpipe daemon and the results stream back to Claude. The MCP server doesn't keep anything. Database access stays inside Screenpipe's daemon and first-party CLI, which own SQLite lifecycle and locking; the MCP server never opens, copies, or mutates the live database, WAL, or shared-memory files. Use MCP tools such as query_recordings for SQL-shaped analysis.

Data storage

Nothing is stored by the MCP server itself. Recordings, OCR text, audio transcripts, and UI events are stored by the Screenpipe app in a SQLite database under ~/.screenpipe/ on your device. Retention is whatever you configure inside the Screenpipe app — typically you control it via the storage settings panel.

Third-party sharing

The MCP server talks to localhost:3030 for tool calls and to Screenpipe's Sentry project for sanitized crash/error reports unless disabled as above. It does not contact Anthropic or send recorded content to Screenpipe's servers. If you choose to enable optional cloud features inside the Screenpipe app itself (e.g. cloud sync, cloud AI), those are governed by the Screenpipe app's privacy policy, not this MCP server's data flow.

Retention

The MCP server has no persistent state. The data your Screenpipe app captures is retained according to your Screenpipe storage configuration and is deletable at any time (rm -rf ~/.screenpipe removes everything).

Source code

The Screenpipe MCP server is source-available under the Screenpipe Commercial License and the entire source is public at https://github.com/screenpipe/screenpipe/tree/main/packages/screenpipe-mcp. Every line is auditable.

Contact

Questions or concerns: open an issue at https://github.com/screenpipe/screenpipe/issues or reach out via https://screenpi.pe.

Saved personal workflows

list-workflows searches the desktop Workflows catalog; get-workflow returns ordered steps, gaps and historical accessibility evidence through the same authenticated API. Both tools are available in stdio and HTTP modes. The running desktop engine must support /workflows; an older engine returns an error. Connected clients can use this context to plan automation, but retrieval neither authorizes nor executes actions. Captured bounds/IDs must be resolved again in the live app. Missing or expired capture and approximate frame matches are explicit. No automatic upload to ChatGPT, Claude or other services is introduced.

The API exposes GET /workflows?q=...&limit=20&offset=0 and GET /workflows/{id}?include_automation=true in OpenAPI. Workflow IDs derive from title, trigger and outcome, so rediscover after a rename. Results omit embedded screenshot bytes. Detail includes up to 30 stages and 100 context nodes per frame, with explicit truncation. frame-context with purpose="automation", node_offset and node_limit (maximum 500) retrieves further nodes while preserving roles, normalized monitor bounds, automation IDs, state and structural ancestors. The default reading mode is unchanged. get-frame-elements with purpose="automation" remains the compact targeting view.

A workflow's frame ID and timestamp must match the current recorder before node hydration. Missing, expired or mismatched frames remain explicit. A historical control is not proof it was clicked: the action target remains unknown until an agent establishes it from current context and the user's instructions.

The API uses the desktop owner's configured app-local workflows/catalog.json with its validated backup, rather than guessing another installation's path. Unconfigured or unreadable storage returns 503. Existing authentication, vault locking and history access apply. Data-scoped or privacy-filtered pipes are denied rather than receiving an unfiltered synthesized report; explicit frame endpoint denials also apply when hydrating accessibility nodes.

Desktop catalog -> authenticated /workflows -> MCP list-workflows / get-workflow
                          |
                          +-> existing frame metadata/context APIs
                          +-> bundled API skill and generated skill references

Each stage can also return inputSearch arguments for search-content with content_type="input". Input results preserve recorded x/y, keys/modifiers, element role/name and linked frame IDs. Treat the bounded time window as candidate events to verify, not as automatic proof of the workflow's actions.