About
A Python-based MCP server that integrates with Microsoft Fabric APIs, providing intelligent notebook creation, code generation, validation, and performance analysis for PySpark development with LLM support.
Capabilities
Fabric MCP Server
The Fabric MCP Server bridges the gap between Daniel Miessler’s Fabric AI framework and any application that speaks the Model Context Protocol (MCP). Fabric is a modular, prompt‑engineering platform that lets developers build reusable AI workflows—patterns, strategies, and configurations—without touching the underlying LLM. MCP, on the other hand, is an open standard that lets tools such as IDE extensions or chat interfaces securely invoke external services. By exposing Fabric’s REST API through the MCP interface, this server lets developers tap into Fabric’s full suite of capabilities from within their favorite AI‑enabled environments.
At its core, the server translates standard MCP calls into Fabric REST endpoints. A client first discovers available tools (e.g., , ) via MCP’s discovery API. When a user invokes one of these tools, the server forwards the request to a running instance, receives the response, and relays it back in MCP format. This tight coupling means that developers can maintain a single Fabric deployment while multiple tools—code editors, chatbots, or automation scripts—consume its patterns and models as if they were native plugins.
Key features include:
- Pattern execution: Run any Fabric pattern (code refactoring, documentation generation, data analysis) directly from an MCP client.
- Metadata exposure: List available patterns, models, and strategies, and retrieve detailed configuration information.
- Configuration passthrough: Pass user‑defined settings or context to Fabric without duplicating logic.
- Security and isolation: The MCP layer enforces request validation, ensuring only authorized clients can invoke sensitive patterns.
Real‑world scenarios abound: a VS Code extension could let an AI assistant refactor code by calling ; a project management tool might generate sprint backlogs using a Fabric pattern; or a data science notebook could invoke a prompt that normalizes datasets—all without leaving the MCP ecosystem. Developers benefit from Fabric’s rich prompt library while keeping their tooling stack simple and compliant with the MCP standard.
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