Fabric MCP Server

Overview

The fabric‑mcp‑server is a dedicated Model Context Protocol (MCP) endpoint that transforms the rich collection of Fabric patterns into actionable tools for AI assistants such as Claude. By exposing each pattern as a discrete MCP tool, the server allows developers to invoke sophisticated data‑processing and transformation workflows directly from within their AI‑driven tasks. This eliminates the need to manually script or orchestrate Fabric jobs, enabling a seamless blend of human intent and automated pattern execution.

For developers who already use Cline to orchestrate AI workflows, the server plugs straight into the existing workflow engine. Once configured, a user can simply add to a prompt or select a pattern from the tool palette, and the assistant will send an MCP request that triggers the corresponding Fabric routine. The response is returned as structured data, which can then be consumed by subsequent steps—such as generating summaries, visualizations, or analytical reports—without leaving the AI context. This tight integration makes it possible to chain together multiple patterns in a single prompt, creating complex pipelines that would otherwise require manual scripting.

Key capabilities of the server include:

• Pattern discovery: All Fabric patterns (e.g., , , ) are automatically registered as MCP tools, giving developers instant access to a broad repertoire of pre‑built logic.
• Execution isolation: Each pattern runs in its own isolated environment, ensuring that state or side effects do not leak between calls.
• Extensibility: New patterns can be added to the Fabric repository, and they will become available without additional configuration—only a server restart is needed.
• Cross‑platform support: The server can be launched via Node.js on Windows, macOS, or Linux, and integrates cleanly with VS Code’s Cline extension through a simple JSON configuration.

Typical use cases include:

• Automated report generation: A user can prompt the assistant to “summarize this dataset” and receive a concise overview generated by the pattern.
• Data quality checks: The tool can validate claims data before it enters downstream processes, reducing manual QA effort.
• Visualization: By invoking , developers can instantly generate diagrammatic representations of complex relationships, ideal for documentation or stakeholder communication.

The server’s standout advantage lies in its ability to bridge the gap between AI intent and domain‑specific execution logic. Rather than reimplementing business rules in every prompt, developers can rely on a single, centrally managed repository of patterns. This promotes consistency, reduces duplication, and accelerates the deployment of AI‑enhanced workflows across teams.