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mediar-ai/screenpipe

Screenpipe is a local-first infrastructure layer designed to solve the 'context gap' in AI agent development. Instead of relying on manual data entry or brittle browser extensions, it provides a continuous, timestamped stream of your screen and audio data, indexed via SQL and vector embeddings. For developers, this means you can build agents that don't just follow prompts, but actually 'remember' everything you've seen and heard on your machine. It bridges the gap between raw OS activity and LLM reasoning by offering semantic search over your entire desktop history. Unlike cloud-based scrapers that raise privacy concerns and latency issues, Screenpipe operates locally, making it a robust choice for building privacy-centric, context-aware tools. Integration is straightforward through its NextJS plugin ecosystem, allowing you to trigger complex workflows based on specific visual or auditory events.

Open ecosystemFile System
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01 / SERVER DETAIL

Server detail

Screenpipe is a local-first infrastructure layer designed to solve the 'context gap' in AI agent development. Instead of relying on manual data entry or brittle browser extensions, it provides a continuous, timestamped stream of your screen and audio data, indexed via SQL and vector embeddings. For developers, this means you can build agents that don't just follow prompts, but actually 'remember' everything you've seen and heard on your machine. It bridges the gap between raw OS activity and LLM reasoning by offering semantic search over your entire desktop history. Unlike cloud-based scrapers that raise privacy concerns and latency issues, Screenpipe operates locally, making it a robust choice for building privacy-centric, context-aware tools. Integration is straightforward through its NextJS plugin ecosystem, allowing you to trigger complex workflows based on specific visual or auditory events.

An MCP server that exposes mediar-ai/screenpipe capabilities to MCP-compatible AI clients.

CapabilitiesFile SystemMCPOpen source
ProtocolModel Context Protocol
Developer ecosystemOpen ecosystem
LicenseMIT / check source
SourceGitHub
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Collections featuring this MCP

Popular MCP2026.03.30Listed
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02 / TOOL TESTING

Tool testing

AVAILABLE TOOL

mediar-ai-screenpipe

Callable

Call the MCP capabilities provided by mediar-ai/screenpipe and return a structured result.

PARAMETERS
inputPass arguments according to the server tool schema.
Tip: the tool may truncate responses. Use pagination parameters such as start_index to read long content in chunks.
03 / SERVICE CONFIGURATION

Connection modes

Remote
{
  "mcpServers": {
    "mediar-ai/screenpipe": {
      "url": "Generated by the provider after deployment"
    }
  }
}

The Remote endpoint is generated by the provider after deployment; this page does not fabricate an unusable endpoint.

03 / WORKFLOW

How to use

  1. 01
    Step 1

    Review server capabilities and permission scope.

  2. 02
    Step 2

    Copy the install command or JSON configuration.

  3. 03
    Step 3

    Run a small connection test in your client.

  4. 04
    Step 4

    Adopt it long term only after reviewing access and maintenance.

04 / DISCUSSIONS

Discussions

Use this space to keep checking source information, usage experience and maintenance status.

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