Fabric Mcp
Fabric MCP Server: Seamlessly integrate Fabric AI capabilities into MCP-enabled tools like IDEs and chat interfaces.
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No install snippet on purpose. A working MCP config is a command, its arguments and an environment block — the last two are where API keys live, so this catalogue never stores them and cannot publish them. Follow the link above for the authors' own instructions.
[![License: MIT][mit_license]][mit_license_link]
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| [![Main Tests][main_tests]][main_tests_link] | [![Main Publish][main_publish]][main_publish_link] | [![Develop Tests][develop_tests]][develop_tests_link] | [![Develop Publish][develop_publish]][develop_publish_link] |
Connect the power of the Fabric AI framework to any Model Context Protocol (MCP) compatible application.
This project implements a standalone server that bridges the gap between [Daniel Miessler's Fabric framework][fabricGithubLink] and the [Model Context Protocol (MCP)][MCP]. It allows you to use Fabric's patterns, models, and configurations directly within MCP-enabled environments like IDE extensions or chat interfaces.
Imagine seamlessly using Fabric's specialized prompts for code explanation, refactoring, or creative writing right inside your favorite tools!
Table of Contents
What is this?
- Fabric: An open-source framework for augmenting human capabilities using AI, focusing on prompt engineering and modular AI workflows.
- MCP: An open standard protocol enabling AI applications (like IDEs) to securely interact with external tools and data sources (like this server).
- Fabric MCP Server: This project acts as an MCP server, translating MCP requests into calls to a running Fabric instance's REST API (
fabric --serve).
Key Goals & Features (Based on Design)
- Seamless Integration: Use Fabric patterns and capabilities directly within MCP clients without switching context.
- Enhanced Workflows: Empower LLMs within IDEs or other tools to leverage Fabric's specialized prompts and user configurations.
- Standardization: Adhere to the open MCP standard for AI tool integration.
- Leverage Fabric Core: Build upon the existing Fabric CLI and REST API without modifying the core Fabric codebase.
- Expose Fabric Functionality: Provide MCP tools to list patterns, get pattern details, run patterns, list models/strategies, and retrieve configuration.
- Expose Pattern Prompts: Fabric patterns are also exposed as MCP prompts so users can discover and invoke reusable pattern templates directly through prompt-aware clients.
How it Works
From the project's README.