fabric-mcp 0.2.2

MCP Server for Fabric AI Framework

Fabric MCP Server

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 and the Model Context Protocol (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!

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.

How it Works

1. An MCP Host (e.g., an IDE extension) connects to this Fabric MCP Server.
2. The Host discovers available tools (like fabric_run_pattern) via MCP's list_tools() mechanism.
3. When the user invokes a tool (e.g., asking the IDE's AI assistant to refactor code using a Fabric pattern), the Host sends an MCP request to this server.
4. The Fabric MCP Server translates the MCP request into a corresponding REST API call to a running fabric --serve instance.
5. The fabric --serve instance processes the request (e.g., executes the pattern).
6. The Fabric MCP Server receives the response (potentially streaming) from Fabric and translates it back into an MCP response for the Host.

Project Status

This project is currently in the design phase. The core architecture and proposed tools are outlined in the High-Level Design Document.

Next Steps:

• Select implementation language (Go/Python) and MCP library.
• Implement the standalone MCP server.
• Define detailed handling for streaming, variables, attachments, and errors.
• Gather community feedback.

Getting Started

These instructions will get you a copy of the project up and running on your local machine for development and testing purposes.

Prerequisites

• Python >= 3.10
• uv (Python package and environment manager)

Installation

1. Clone the repository:

   git clone https://github.com/ksylvan/fabric-mcp.git
   cd fabric-mcp
   

2. Install dependencies using uv sync:

   uv sync --dev
   

   This command ensures your virtual environment matches the dependencies in pyproject.toml and uv.lock, creating the environment on the first run if necessary.

3. Activate the virtual environment (uv will create it if needed):

   • On macOS/Linux:

     source .venv/bin/activate
     

   • On Windows:

     .venv\Scripts\activate
     

Now you have the development environment set up!

Contributing

Feedback on the design document is highly welcome! Please open an issue to share your thoughts or suggestions. Contribution guidelines will be added as the project progresses.

License

Copyright (c) 2025, Kayvan Sylvan Licensed under the MIT License.