Figma Context MCP
Give your coding agent access to your Figma data. Implement designs in any framework in one-shot.
Beschreibung vom Entwickler (englisches Original)
Search your local screen recordings, audio transcripts, and computer activity from screenpipe.
Diese drei werden nie zu einer Punktzahl zusammengefasst: Meinungen, Messungen und Beliebtheit sind verschiedene Dinge.
Für diesen Server haben wir noch keine Zusammenfassung in einfacher Sprache geschrieben. Unten stehen die Fakten, die wir aus der Registry gelesen haben.
Wähle die KI-App, die du nutzt, und kopiere die Einstellungen.
Du brauchst
Welche App nutzt du?
Wähle oben deine App. Wir merken sie uns, damit der Button beim nächsten Mal „In … installieren“ anzeigt.
Öffne Claude Desktop → Settings → Developer → Edit Config und füge dies in claude_desktop_config.json ein
{
"mcpServers": {
"screenpipe": {
"command": "npx",
"args": [
"-y",
"screenpipe-mcp"
]
}
}
}Führe das in deinem Terminal aus
claude mcp add screenpipe -- npx -y screenpipe-mcpIn .cursor/mcp.json in deinem Projekt oder in ~/.cursor/mcp.json einfügen
{
"mcpServers": {
"screenpipe": {
"command": "npx",
"args": [
"-y",
"screenpipe-mcp"
]
}
}
}Dein Browser fragt, ob er die App öffnen darf, dann zeigt die App die Einstellungen und bittet dich um Bestätigung. Schlüsselwerte bleiben leer, damit du sie selbst einträgst.
In .vscode/mcp.json in deinem Projekt einfügen
{
"servers": {
"screenpipe": {
"type": "stdio",
"command": "npx",
"args": [
"-y",
"screenpipe-mcp"
]
}
}
}Dein Browser fragt, ob er die App öffnen darf, dann zeigt die App die Einstellungen und bittet dich um Bestätigung. Schlüsselwerte bleiben leer, damit du sie selbst einträgst.
Dieser Server läuft auf deinem Computer, und ChatGPT kann sich nur mit Servern im Internet verbinden. Wähle eine andere App.
Deine Auswahl bleibt in einem Cookie auf diesem Gerät; kein Konto nötig.
Diese Einstellungen werden aus dem Registry-Eintrag des Entwicklers erzeugt. Wenn in der README etwas anderes steht, folge der README.
Läuft auf deinem Computer; die Tool-Liste erscheint nach der Installation in deiner KI-App.
Wir prüfen Informationen, die jeder sehen kann; den Code haben wir nicht gelesen. Checks, die nicht zutreffen, werden nicht mitgezählt. · Geprüft am 6. Nov. 2026
Der Registry-Name wurde mit dem GitHub-Konto screenpipe bestätigt, dem das Repo gehört
Dieses Repo haben wir noch nicht von GitHub gelesen
Dieses Repo haben wir noch nicht von GitHub gelesen
Dieser Server läuft auf deinem Computer; es gibt keinen Endpunkt im Internet
Läuft auf deinem Computer und nutzt stattdessen lokale Einstellungen
Läuft auf deinem Computer; Tool-Listen können wir nur von Endpunkten im Internet lesen
Verglichen mit allen aktiven Servern dieser Kategorie in der Registry. Sterne zählen nur, wenn dem Herausgeber das Repo gehört.
Er hat 1 von 1 zutreffenden Basischecks bestanden. Den Code haben wir nicht gelesen. Bevor du einen Schlüssel oder Token hinzufügst, sieh nach, welche Zugriffe der Server verlangt, und nutze einen Schlüssel, der nur die Berechtigungen hat, die du brauchst.
Die Registry nennt keinen erforderlichen Schlüssel, manche Server bitten dich aber bei der ersten Nutzung, dich anzumelden.
Er läuft auf deinem Computer. Deine KI-App startet ihn bei Bedarf, und die Daten laufen nicht über die Server des Entwicklers (außer der Server ruft selbst einen Onlinedienst auf).
Das GitHub-Repo wurde zuletzt – geändert, und die neueste Version (0.20.4) kam vor 4 Tagen in die Registry.
Apps, die MCP unterstützen, etwa Claude Desktop, Claude Code, Cursor und VS Code. Außerdem brauchst du Node.js auf deinem Computer.
In derselben Kategorie: Figma Context MCP, Claude Real Video, Figma MCP Server
Server derselben Kategorie von anderen Entwicklern, sortiert nach Sternen.
So liest du eine Karte:★ GitHub-SterneiDrei Säulen: Bewertungen · Checks · NutzungiAktualisiert = neueste Version in der Registry veröffentlichti
Give your coding agent access to your Figma data. Implement designs in any framework in one-shot.
Let any LLM watch a video locally — and search everything it has ever watched.
The Figma MCP server brings Figma design context directly into your AI workflow.
Unverändert aus dem Repo des Entwicklers übernommen. PopNx MCP hat den Inhalt weder geprüft noch empfiehlt es ihn. HTML, Bilder und Skripte wurden entfernt. Links führen von dieser Website weg.
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.
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.
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 capability | stdio | HTTP |
|---|---|---|
| Search with source references and text caps | search-content | search_content |
| Local agent chat search | search-chats | search-chats |
| Bundled guides | screenpipe-skills | screenpipe-skills |
| Frame inspection and saved workflows | Available | Not exposed |
| Enterprise team tools | With team configuration | Not exposed |
| Call diagnostics | Best effort | Best 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.
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.
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:
npx or PATH dependency, and ~3× faster cold start), andenv, 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.
If you're not using the desktop app, edit your Claude Desktop config:
~/Library/Application Support/Claude/claude_desktop_config.json%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.
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
npx -y screenpipe-mcp --http --listen-on-lan --api-key $(openssl rand -hex 16)
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-mcppackage; use--httpas shown above, or invoke the
bin directly with
npx -p screenpipe-mcp screenpipe-mcp-http.
The server exposes:
http://localhost:3031/mcp — Streamable HTTP transport (POST for requests, GET for SSE stream)http://localhost:3031/health — always unauthenticated, for monitorsOptions:
| Flag | Description | Default |
|---|---|---|
--port | Port for the MCP HTTP server | 3031 |
--screenpipe-port | Port where screenpipe API is running | 3030 |
--listen-on-lan | Bind 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
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