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tube-bridge

tube-bridge is a self-hosted MCP server that lets you treat YouTube as a research data source rather than just a video platform. It exposes 17 tools covering search, transcripts, timestamped video frames, comments, and a local semantic corpus you can query privately. The server runs locally, so your queries and any cached content stay on your machine, which matters for both privacy and avoiding rate limits. Integration follows the standard MCP protocol, so any MCP-capable client (Claude Desktop, custom agents, editors with MCP support) can discover and call its tools without extra glue code. Compared to ad-hoc scraping, tube-bridge normalizes transcript and comment data into structured outputs, supports timestamped frame extraction for visual evidence, and adds a local vector store so you can build private, semantic search over collected content. It's aimed at researchers, analysts, and developers who need repeatable, scriptable access to YouTube data without depending on external APIs or sending queries to third-party services. Setup is via the GitHub repo, and you'll need a local environment that can run the server and store the corpus.

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01 / SERVER DETAIL

Server detail

tube-bridge is a self-hosted MCP server that lets you treat YouTube as a research data source rather than just a video platform. It exposes 17 tools covering search, transcripts, timestamped video frames, comments, and a local semantic corpus you can query privately. The server runs locally, so your queries and any cached content stay on your machine, which matters for both privacy and avoiding rate limits. Integration follows the standard MCP protocol, so any MCP-capable client (Claude Desktop, custom agents, editors with MCP support) can discover and call its tools without extra glue code. Compared to ad-hoc scraping, tube-bridge normalizes transcript and comment data into structured outputs, supports timestamped frame extraction for visual evidence, and adds a local vector store so you can build private, semantic search over collected content. It's aimed at researchers, analysts, and developers who need repeatable, scriptable access to YouTube data without depending on external APIs or sending queries to third-party services. Setup is via the GitHub repo, and you'll need a local environment that can run the server and store the corpus.

An MCP server that exposes tube-bridge capabilities to MCP-compatible AI clients.

CapabilitiesSearchMCPOpen source
ProtocolModel Context Protocol
Developer ecosystemOpen ecosystem
LicenseMIT / check source
SourceGitHub
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02 / TOOL TESTING

Tool testing

AVAILABLE TOOL

tube-bridge

Callable

Call the MCP capabilities provided by tube-bridge 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": {
    "tube-bridge": {
      "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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