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Rocketride Server

by rocketride-org

Exposes RocketRide AI pipelines as MCP tools for Claude, Cursor, and Windsurf in self-hosted deployments.

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Install Rocketride Server

Pick your client, then add it in one click or paste the config.

STDIOPython
~/Library/Application Support/Claude/claude_desktop_config.json
claude_desktop_config.json
{
  "mcpServers": {
    "rocketride-org-rocketride-server": {
      "command": "uvx",
      "args": [
        "rocketride"
      ]
    }
  }
}

Settings → Developer → Edit Config, merge this into mcpServers, then fully quit and reopen Claude Desktop.

Install not yet sandbox-tested— we haven't run this command through our automated sandbox yet. Not a red flag; we're working through the catalog.

Overview

rocketride-org/rocketride-server exposes RocketRide AI pipelines as callable MCP tools for Claude, Cursor, and Windsurf. It connects assistants to RocketRide’s self-hosted pipeline runtime, which supports multi-LLM workflows and data-processing nodes. Reach for it when an agent needs to invoke reusable RocketRide workflows from an IDE or MCP-compatible client. RocketRide pipelines can be built visually, represented as portable JSON, and run on local infrastructure.

Use cases

  • Invoke reusable RocketRide workflows from an AI coding assistant
  • Expose multi-step LLM pipelines as MCP tools
  • Run document-processing and retrieval workflows on local infrastructure
  • Connect IDE-based agents to self-hosted AI pipelines

Key features

  • Exposes RocketRide pipelines as MCP tools
  • Supports Claude, Cursor, and Windsurf
  • Works with multi-LLM pipeline workflows
  • Supports local and self-hosted execution
  • Uses portable JSON pipeline definitions
  • Accesses RocketRide nodes for vector search, OCR, NER, and embeddings

Security & health

What it can reach and how reliably it runs — from our own checks, not the maintainer’s claims.

ActiveLast checked Today

Recent health check succeeded.

26/30 checks healthy over the last 77d

Supply-chain signal

No high-severity advisories surfaced by our automated scan.

Critical 0High 0Medium 0Low 0

Scanned 4d ago via OSV.dev · rocketride (PyPI)

Adoption & maintenance

Factual signals from GitHub, npm, and our automated checks — not a rating.

GitHub stars17kStargazers on the source repository.
Last commitTodayMost recent push to the default branch.
Directory activity6 viewsConfig copies, upvotes, and views on AllMCPs.

How Rocketride Server works

What rocketride-org/rocketride-server does

rocketride-org/rocketride-server makes RocketRide AI pipelines available to MCP clients as callable tools. Instead of embedding each workflow directly in an assistant integration, a client can invoke a pipeline exposed through RocketRide. This is suited to teams that already build workflows with RocketRide or want agents to access repeatable AI and data-processing operations.

RocketRide is an open-source pipeline builder and runtime designed for AI and machine-learning workloads. Its pipeline system covers LLM providers, vector databases, OCR, named-entity recognition, embeddings, chunking, and related processing tasks. The MCP server provides the connection point between those workflows and supported AI assistants.

How it works

Pipelines are composed in RocketRide and stored as portable JSON definitions. They can be created visually in VS Code, tested, observed, and executed by RocketRide’s runtime. Through MCP, the resulting workflows are presented to an assistant as tools that can be called during an interaction.

Execution remains tied to the RocketRide environment hosting the pipelines. The platform supports local operation and self-hosted deployment, so teams can run workflows on their own infrastructure rather than sending pipeline execution through a hosted service. RocketRide also supports Python and TypeScript SDKs for applications that need to invoke pipelines outside an MCP client.

The underlying runtime supports multiple LLM providers and other pipeline nodes. This allows an exposed workflow to combine model calls with retrieval, document processing, or other configured stages, depending on how the pipeline was built.

Setup and configuration

The server is intended for self-hosted use with RocketRide. RocketRide can run locally, and its MCP documentation provides the connection path for exposing pipelines to clients. The project links to setup documentation at docs.rocketride.org/connect/mcp/http.

Client support called out for this server includes Claude, Cursor, and Windsurf. The broader RocketRide project also provides a VS Code extension for designing and running pipelines, but pipeline authoring and MCP client connectivity are separate parts of the workflow.

Tools and capabilities

  • Expose RocketRide pipelines as MCP-callable tools.
  • Run reusable AI workflows from Claude, Cursor, or Windsurf.
  • Use pipelines that combine multiple LLM providers and data-processing nodes.
  • Work with workflows involving vector databases, OCR, NER, embeddings, and chunking.
  • Keep pipeline definitions portable as JSON files.
  • Execute workflows on local or self-managed infrastructure.

RocketRide advertises more than 100 pipeline nodes, spanning more than 15 LLM providers and nine vector databases. It also includes support for multi-agent workflows through CrewAI and LangChain at the platform level.

Limitations and notes

The server exposes capabilities defined by the RocketRide pipelines available to it; it is not a general-purpose collection of unrelated MCP tools. The useful operations, model providers, databases, and processing stages therefore depend on the pipelines and nodes configured in the RocketRide environment.

RocketRide is designed for self-hosted operation, which places runtime and deployment responsibilities on the operator. The project is MIT-licensed and supports local, Docker, on-premises, and RocketRide Cloud deployment options for the broader platform.

Read the full README View source on GitHub

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Frequently Asked Questions about Rocketride Server

rocketride-org/rocketride-server is an MCP server that connects MCP-compatible clients to RocketRide AI pipelines and exposes those pipelines as callable tools. It lets assistants such as Claude, Cursor, and Windsurf invoke workflows built in RocketRide, including pipelines using its multi-LLM and data-processing capabilities, while keeping deployment self-hosted.

Use RocketRide’s MCP server setup documentation to connect a client to a self-hosted RocketRide environment. RocketRide supports local operation, and its MCP connection documentation is available at docs.rocketride.org/connect/mcp/http.

Yes, rocketride-org/rocketride-server is described as supporting Cursor. Once connected to the RocketRide MCP server, Cursor can use the RocketRide pipelines exposed as tools.

Yes, it can expose RocketRide pipelines built with the platform’s multi-provider LLM support. RocketRide describes more than 15 supported LLM providers, allowing pipeline authors to configure workflows around different models.

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