RocketRide MCP Server

Let AI assistants run your RocketRide pipelines via the Model Context Protocol.

Quick Start

pip install rocketride-mcp

Configure your MCP client to use the server (see examples below), then ask your AI assistant to process files through your running RocketRide pipelines.

How It Works

The MCP server connects to a running RocketRide engine and dynamically exposes your pipelines as MCP tools. When an AI assistant calls a tool, the server sends the file to the corresponding pipeline and returns the result.

AI Assistant (Claude, Cursor, ...)
        |
   MCP Protocol
        |
  rocketride-mcp server
        |
   WebSocket (DAP)
        |
  RocketRide Engine
        |
   Your Pipelines

Running pipelines are discovered automatically - start a pipeline in VS Code or via the SDK, and it appears as a callable tool in your AI assistant.

What is RocketRide?

RocketRide is an open-source, developer-native AI pipeline platform. It lets you build, debug, and deploy production AI workflows without leaving your IDE -- using a visual drag-and-drop canvas or code-first with TypeScript and Python SDKs.

• 100+ ready-to-use nodes - 15+ LLM providers, 9 vector databases, OCR, NER, PII anonymization, and more
• High-performance C++ engine - production-grade speed and reliability
• Deploy anywhere - locally, on-premises, or self-hosted with Docker
• MIT licensed - fully open-source, OSI-compliant

How pipelines become tools

Every pipeline you start in RocketRide is automatically registered as an MCP tool by the server — no extra configuration required.

When you start a pipeline (via the VS Code extension, the CLI, or an SDK), the engine assigns it a task token. The MCP server discovers all running tasks for your API key and exposes each one as a callable tool. The tool name is derived from the pipeline name; the tool schema is derived from the pipeline's input lanes.

You start a pipeline          →  engine assigns a task token
                                    ↓
rocketride-mcp discovers it   →  registers it as an MCP tool
                                    ↓
Claude calls the tool         →  MCP server forwards the request to the pipeline
                                    ↓
Pipeline processes it         →  result streamed back to Claude

Stop the pipeline and the tool disappears from the next tool-list refresh. Start a new pipeline and it appears automatically.

Worked example

1. Start the MCP server (if not already running via a client config):

export ROCKETRIDE_URI=ws://localhost:5565
export ROCKETRIDE_AUTH=your-api-key
rocketride-mcp

2. Start a pipeline — for example, a simple chat pipeline (chat.pipe):

{
  "nodes": [
    { "id": "source_1", "provider": "webhook" },
    {
      "id": "llm_1", "provider": "llm_openai",
      "config": { "profile": "openai-4o-mini", "apikey": "${OPENAI_API_KEY}" },
      "input": [{ "lane": "questions", "from": "source_1" }]
    },
    { "id": "target_1", "provider": "response",
      "input": [{ "lane": "answers", "from": "llm_1" }] }
  ]
}

rocketride start ./chat.pipe

3. Ask Claude to use it. Open Claude Desktop (configured with the mcpServers block above) and type:

Use the RocketRide pipeline to answer: what is the boiling point of water?

Claude discovers the tool, calls it with the question, and returns the answer streamed from your pipeline.

4. Inspect resources — Claude can also list your pipelines and check server status using MCP resources:

Show me the available RocketRide pipelines.

This reads rocketride://pipelines and returns the list of running tasks.

Installation

pip install rocketride-mcp

Requires Python 3.10+ and rocketride >= 1.0.4.

Client Configuration

Claude Desktop

Add to your Claude Desktop config file:

macOS: ~/Library/Application Support/Claude/claude_desktop_config.json Windows: %APPDATA%\Claude\claude_desktop_config.json

{
	"mcpServers": {
		"rocketride": {
			"command": "rocketride-mcp",
			"env": {
				"ROCKETRIDE_URI": "ws://localhost:5565",
				"ROCKETRIDE_AUTH": "your-api-key"
			}
		}
	}
}

Cursor

Add to .cursor/mcp.json in your workspace:

{
	"mcpServers": {
		"rocketride": {
			"command": "rocketride-mcp",
			"env": {
				"ROCKETRIDE_URI": "ws://localhost:5565",
				"ROCKETRIDE_AUTH": "your-api-key"
			}
		}
	}
}

Claude Code

claude mcp add rocketride -- rocketride-mcp

Set ROCKETRIDE_URI and ROCKETRIDE_AUTH in your environment before running.

Command line

# Using the installed entry point
rocketride-mcp

# Or using Python module
python -m rocketride_mcp

Available Tools

Tools are discovered from the RocketRide server (pipelines/tasks available to your account) plus a built-in convenience tool:

• Server tasks - Any pipelines or tasks returned by the server for your API key are exposed as MCP tools. Each tool accepts a filepath argument and sends that file's contents to the corresponding pipeline.
• RocketRide_Document_Processor - A convenience tool that runs the bundled document-parsing pipeline (simpleparser.json) without requiring a pre-started task. Supports multi-modal parsing (text, images, video, tables, audio).

All tools accept a single filepath parameter (path to the file to process). File paths support:

• Absolute and relative paths
• file:// URIs (automatically decoded)
• ~ home directory expansion

Response format

Tool results include both human-readable text and structured data:

• Text content: Confirmation message plus extracted text from the pipeline result
• Structured content: Raw pipeline result in structuredContent.result for programmatic access

MCP Resources

The server exposes three read-only MCP Resources that provide live information about the connected RocketRide engine. Resources use the rocketride:// URI scheme and return JSON payloads.

URI	Name	Description
rocketride://pipelines	Pipeline List	JSON array of all available pipelines (name and description) on the connected server
rocketride://status	Server Status	Connection status, pipeline count, and list of loaded pipeline names
rocketride://nodes	Node Registry	Available pipeline node types and their schemas (via rrext_get_nodes)

Reading resources

In Claude Desktop or any MCP-compatible client, resources are listed automatically. You can also access them programmatically:

# Example: read the pipeline list resource
result = await session.read_resource('rocketride://pipelines')
# Returns: {"pipelines": [{"name": "my-pipeline", "description": "..."}, ...]}

# Example: check server status
result = await session.read_resource('rocketride://status')
# Returns: {"connected": true, "pipeline_count": 3, "pipelines": ["pipe-a", "pipe-b", "pipe-c"]}

# Example: list available node types
result = await session.read_resource('rocketride://nodes')
# Returns: {"nodes": [{"name": "llm_openai", "type": "processor"}, ...]}

When the RocketRide client is not connected, resources return a JSON error payload (e.g. {"pipelines": [], "error": "Client is not connected"}) instead of raising an exception. Unknown URIs raise a ValueError.

MCP Prompt Templates

The server provides three reusable MCP Prompt Templates for common RocketRide operations. These templates generate pre-formatted user messages that can be sent to an LLM.

analyze-document

Analyze a document through a RocketRide pipeline.

Argument	Required	Description
pipeline	Yes	Pipeline name to use for analysis
query	Yes	Analysis question or instruction

Example usage in Claude Desktop:

Select the "analyze-document" prompt, then fill in:

• pipeline: invoice-parser
• query: Extract all line items and totals

This generates the message: "Please analyze the document using the RocketRide pipeline "invoice-parser". Focus on the following: Extract all line items and totals"

chat-with-data

Start a conversation about data processed by RocketRide.

Argument	Required	Description
pipeline	Yes	Pipeline name
question	Yes	Your question about the data

Example usage:

• pipeline: quarterly-reports
• question: What was the revenue growth in Q3?

This generates the message: "I would like to discuss data processed by the RocketRide pipeline "quarterly-reports". My question is: What was the revenue growth in Q3?"

evaluate-pipeline

Evaluate a pipeline's output quality using test data.

Argument	Required	Description
pipeline	Yes	Pipeline to evaluate
test_input	Yes	Test input data
expected_output	No	Expected output for comparison

Example usage:

• pipeline: sentiment-classifier
• test_input: This product is fantastic!
• expected_output: positive

This generates the message: "Evaluate the output quality of the RocketRide pipeline "sentiment-classifier" using the following test input: This product is fantastic! Expected output: positive"

Using prompts programmatically

# List available prompts
prompts = await session.list_prompts()

# Get a rendered prompt
result = await session.get_prompt(
    'analyze-document', arguments={'pipeline': 'my-pipeline', 'query': 'Summarize the key findings'}
)
# result.messages[0].content.text contains the rendered message

SSE Mode

For remote or Docker deployments, the server can run as an HTTP/SSE server instead of stdio:

pip install rocketride-mcp[sse]
export MCP_API_KEY=your-secret-token
rocketride-mcp-sse --host 0.0.0.0 --port 8080

SSE mode supports Bearer token authentication via the MCP_API_KEY environment variable — optional when binding localhost, but mandatory for non-loopback binds (the server refuses to start on hosts like 0.0.0.0 without it). The /health endpoint is always accessible for monitoring.

Configuration

Set these environment variables (required; no config file is used):

Variable	Required	Description
ROCKETRIDE_URI	Yes	WebSocket URI of the RocketRide engine (e.g. ws://localhost:5565)
ROCKETRIDE_AUTH	Yes*	API authentication token
ROCKETRIDE_APIKEY	Yes*	Alternative to ROCKETRIDE_AUTH
MCP_API_KEY	No (loopback) / Yes (non-loopback binds)	Bearer token for SSE server authentication

*Either ROCKETRIDE_AUTH or ROCKETRIDE_APIKEY must be set.

Links

• Documentation
• GitHub
• Discord
• Contributing

License

MIT - see LICENSE.