RocketRide is an AI pipeline orchestration platform. Here's what you can do with this server:
Process Documents: Submit documents via filepath to RocketRide's AI pipeline engine, which can include OCR, NER, PII anonymization, chunking, and embedding
Build & Deploy AI Pipelines: Visually design complex AI/ML workflows using a VS Code canvas, defined as portable JSON and executed by a high-performance multithreaded C++ runtime
Integrate Diverse AI Components: Access 50+ pre-built pipeline nodes supporting 13 LLM providers, 8 vector databases, OCR, NER, embedding models, and more
Orchestrate Multi-Agent Workflows: Build complex agent pipelines with built-in support for CrewAI and LangChain
Automate with Coding Agents: Enable AI agents (e.g., Claude, Cursor) to build, modify, and deploy pipelines via natural language
Monitor & Optimize: Observe running pipelines with tracing, token usage, memory consumption, and execution analytics
Deploy Flexibly: Run locally, on-premises (Docker), or integrate via Python or TypeScript/JS SDKs
Extend Functionality: Develop and publish custom pipeline nodes using Python
Provides built-in support for orchestrating and scaling multi-agent workflows within data processing pipelines.
Enables the orchestration and scaling of AI agents and workflows within data processing pipelines using LangChain.
Design, test, and ship complex AI workflows from a visual canvas, right where you write code.
Drop pipelines into any Python or TypeScript app with a few lines of code, no infrastructure glue required.
Let us handle the infrastructure, or own every layer.
Related MCP server: mcpgate
Connecting takes two lines. Same portable pipeline JSON, now hosted for you:
ROCKETRIDE_URI=https://api.rocketride.ai
ROCKETRIDE_AUTH=your-api-tokenPoint a client at your local engine in one line:
ROCKETRIDE_URI=ws://localhost:5565Feature | Description |
Visual Pipeline Builder | Drag, connect, and configure nodes in VS Code, no boilerplate. Real-time observability tracks token usage, LLM calls, latency, and execution. Pipelines are portable JSON, version-controllable, shareable, and runnable anywhere. |
High-Performance C++ Runtime | Native multithreading purpose-built for the throughput demands of AI and data workloads. No bottlenecks, no compromises for production scale. |
100+ Pipeline Nodes | 15+ LLM providers, 9 vector databases, OCR, NER, PII anonymization, chunking strategies, embedding models, and more. All nodes are Python-extensible, build and publish your own. |
Multi-Agent Workflows | Built-in CrewAI and LangChain support. Chain agents, share memory across pipeline runs, and manage multi-step reasoning at scale. |
Coding Agent Ready | RocketRide auto-detects your coding agent: Claude, Cursor, and more. Build, modify, and deploy pipelines through natural language. |
TypeScript, Python & MCP SDKs | Integrate pipelines into native apps, expose them as callable tools for AI assistants, or build programmatic workflows into your existing codebase. |
Zero Dependency Headaches | Python environments, C++ toolchains, Java/Tika, and all node dependencies managed automatically. Clone, build, run, no manual setup. |
One-Click Deploy | Run on Docker, on-prem, or RocketRide Cloud. Production-ready architecture from day one, not retrofitted from a demo. |
Install the extension for your IDE. Search for RocketRide in the extension marketplace:
Click the RocketRide extension in your IDE
Deploy a server - you'll be prompted on how you want to run the server. Choose the option that fits your setup:
Local (Recommended) - This pulls the server directly into your IDE without any additional setup.
On-Premises - Run the server on your own hardware for full control and data residency. Pull the image and deploy to Docker or clone this repo and build from source.
All pipelines are recognized with the *.pipe format. Each pipeline and its configuration are JSON objects - but the extension in your IDE will render within our visual builder canvas.
All pipelines begin with a source node: webhook, chat, or dropper. For specific usage, examples, and inspiration on how to build pipelines, check out our guides and documentation.
Connect input lanes and output lanes by type to properly wire your pipeline. Some nodes like agents or LLMs can be invoked as tools for use by a parent node as shown below:
You can run a pipeline from the canvas by pressing the ▶ button on the source node or from the Connection Manager directly.
Deploy your pipelines - pick the path that fits:
Docker - Download the RocketRide server image and create a container. Requires Docker to be installed.
docker pull ghcr.io/rocketride-org/rocketride-engine:latest
docker create --name rocketride-engine -p 5565:5565 ghcr.io/rocketride-org/rocketride-engine:latestLocal Deployment - Download your preferred runtime as a standalone process from the Deploy page in the Connection Manager.
RocketRide Cloud - Skip the setup and ship straight to managed hosting. Same portable pipeline JSON, zero infrastructure to run, from prototype to production. Get started
Run your pipelines as standalone processes or integrate them into your existing Python and TypeScript/JS applications utilizing our SDK.
Selecting running pipelines allows for in-depth analytics. Trace call trees, token usage, memory consumption, and more to optimize your pipelines before scaling and deploying. Find the models, agents, and tools best fit for your task.
RocketRide is built by a growing community of contributors. Whether you've fixed a bug, added a node, improved docs, or helped someone on Discord, thank you. New contributions are always welcome - check out our contributing guide to get started.
Convenience tool: RocketRide Document Processor
| Name | Required | Description | Default |
|---|---|---|---|
| filepath | Yes | Path to file to process |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It fails to indicate whether processing is destructive, what output is generated, or any side effects, leaving the agent with no safety or operational context.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
While only four words, the description is empty of actionable information. This represents under-specification rather than efficient front-loading; the 'Convenience tool' prefix wastes space without conveying value.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Even with only one parameter, the description inadequately explains what 'processing' entails. For a tool implying document transformation, the lack of behavioral context, output expectations, or RocketRide-specific functionality leaves critical gaps.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 100% description coverage for the single 'filepath' parameter. The description adds no additional semantic context about the parameter (e.g., supported formats, relative vs absolute paths), meeting the baseline for high-coverage schemas.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description restates the tool name with the filler prefix 'Convenience tool:', failing to specify what processing actually occurs (e.g., conversion, extraction, validation) or provide a specific action verb beyond the implied 'process'.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance provided on when to use this tool, prerequisites for the file path, or expected outcomes. With no siblings, differentiation is moot, but general usage context is absent.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Recent tool additions, removals, and schema changes observed during successful MCP inspections. Dates show when Glama detected each change.
v0.1.0RocketRide_Document_ProcessorWith only one tool, there is no possibility of confusion or overlap between tools. The tool has a single, distinct purpose, making disambiguation trivial.
Since there is only one tool, naming consistency is inherently perfect. The tool name follows a clear pattern, and there are no other tools to compare it against for inconsistency.
A single tool is generally too few for most server purposes, as it limits functionality and may indicate an incomplete or overly narrow scope. For a server named 'RocketRide', which suggests broader capabilities, one tool feels insufficient.
With only one tool, the surface is severely incomplete. It's impossible to assess coverage for any meaningful domain, and the tool's vague description ('Convenience tool') offers no insight into what operations it supports, suggesting major gaps.
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