Rocketride Server

What rocketride-org/rocketride-server MCP server does

The rocketride-org/rocketride-server MCP server makes RocketRide pipelines available to MCP-compatible coding assistants and AI applications as callable tools. RocketRide is an open-source pipeline builder and runtime for AI and machine-learning workloads. Its pipelines are stored as portable JSON objects using the .pipe file format, so they can be version-controlled, shared, and executed in different environments.

A pipeline can combine model and data-processing steps rather than representing only a single model request. The documented node set includes LLM providers, vector databases, OCR, named-entity recognition, personally identifiable information anonymization, text chunking, and embedding models. The project also documents support for multi-agent workflows through CrewAI and LangChain.

How it works

RocketRide pipelines are designed in a visual builder available through the RocketRide IDE extension, with VS Code identified as the primary environment in the documentation. The extension renders .pipe files on a visual canvas and provides execution observability, including token usage, model calls, latency, and run information.

The runtime is implemented in C++ and uses native multithreading. Pipeline nodes can be extended with Python, while applications can integrate pipelines through the project’s Python or TypeScript SDKs. The MCP server provides the agent-facing path: an assistant can call configured RocketRide pipelines as tools instead of requiring each workflow to be reimplemented in the client.

The project describes three pipeline source types: webhook, chat, and dropper. Pipelines can run on local infrastructure or on-premises hardware. Docker deployment is also documented as an option. RocketRide Cloud is mentioned as planned rather than as an available deployment target.

Setup and configuration

The documented quick start begins by installing the RocketRide extension from an IDE extension marketplace, then opening the extension and deploying a server. The extension offers a local deployment path that pulls the server into the IDE, or an on-premises path for users who want to run it on their own hardware and control data residency.

For on-premises use, the README points to Docker deployment or building the repository from source. The supplied material does not specify an MCP configuration file, command-line invocation, required environment variables, authentication settings, or provider-specific credentials. Those details should be confirmed in the project documentation before automating deployment.

Tools and capabilities

The rocketride-org/rocketride-server MCP server supports:

• Exposing RocketRide pipelines as callable assistant tools
• Running JSON-defined .pipe workflows
• Using pipelines with Claude, Cursor, and Windsurf
• Connecting workflows to multiple LLM providers and vector databases
• Processing OCR, entities, PII anonymization, chunks, and embeddings
• Building multi-agent flows with CrewAI and LangChain
• Integrating workflows into Python and TypeScript applications
• Running locally, on premises, or through Docker

Limitations and notes

The available material does not list the MCP tool names or describe their input and output schemas. It also does not establish which individual model providers or vector databases require separate credentials. Although the repository is open source, pipeline operation may still depend on the services and models selected within a workflow. RocketRide Cloud is described as coming soon, so it should not be treated as an available hosted option based on this README alone.