Overview

The Node‑RED MCP server is a lightweight implementation of the Model Context Protocol (MCP) that allows AI assistants to tap into the rich workflow capabilities of Node‑RED. By exposing Node‑RED flows as MCP resources, developers can let an AI model act as a dynamic orchestrator—invoking pre‑built logic, data pipelines, or external services without writing code. This bridges the gap between low‑code automation platforms and conversational AI, enabling seamless integration of visual workflows into natural language interactions.

At its core, the server translates Node‑RED’s event‑driven architecture into MCP resources. Each flow or subflow becomes an MCP resource that the AI can request, and each node within those flows can be exposed as a tool with defined input parameters. The server also handles prompt injection, allowing developers to customize the context passed to the AI for each invocation. By doing so, it eliminates the need for custom adapters or REST wrappers; the MCP server automatically discovers and exposes Node‑RED nodes, making them discoverable through standard MCP discovery mechanisms.

Key capabilities include:

• Dynamic resource exposure – Node‑RED flows are automatically published as MCP resources, reflecting real‑time changes in the editor.
• Tool integration – Nodes that accept inputs (e.g., HTTP request, database query) are exposed as tools with typed arguments, enabling the AI to construct precise calls.
• Prompt templating – Developers can define prompt templates that inject contextual information or instructions into the AI’s prompt, ensuring consistent behavior across calls.
• Sampling control – The server supports MCP sampling parameters, allowing fine‑tuned generation control directly from the Node‑RED side.
• Security and authentication – MCP’s built‑in authentication can be leveraged to restrict access, ensuring only authorized AI agents can invoke sensitive flows.

Typical use cases span from automated customer support—where an AI agent triggers a Node‑RED flow to retrieve ticket data—to IoT orchestration, where conversational commands result in real‑time device control. In a DevOps context, the MCP server can expose monitoring dashboards or alerting pipelines, letting an AI assistant query metrics and trigger remediation flows on demand. The integration is straightforward: the AI client discovers resources, selects a tool, and supplies arguments; the server executes the corresponding Node‑RED node chain and returns results back to the assistant.

What sets this MCP server apart is its zero‑code exposure model. Developers who already own Node‑RED flows can instantly make them available to AI assistants without writing adapters or API wrappers. The server’s tight coupling with Node‑RED’s visual editor also means updates to flows are immediately reflected in the MCP interface, keeping AI integrations in sync with evolving business logic. This makes it an ideal bridge for teams that rely on low‑code automation but want to unlock the conversational intelligence of modern AI assistants.