Fabric MCP Server
About
Fabric MCP Server: Seamlessly integrate Fabric AI capabilities into MCP-enabled tools like IDEs and chat interfaces.
Details
- Transport
- SSE
- License
- MIT
Explore
- 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.
Setting up with Highlight
This MCP is not yet compatible with Highlight’s one-click setup. However, you can still use it with Highlight by following these steps:
- Download and install Highlight from highlightai.com/download
- Navigate to the plugins tab and select "Add Custom Plugin"
-
Configure the plugin with the settings below
Plugin Name
Fabric MCP ServerCommand (node, npx, python, etc.)Please refer to the README for specific instructions on how to obtain API keys or other required environment variables.
- Enable "Start Automatically" if you want the plugin to start when Highlight launches
From the repository
These instructions will get you a copy of the project up and running on your local machine for development and testing purposes.
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!
If you just want to use the fabric-mcp server without developing it, you can install it directly from PyPI:
The fabric-mcp server can be configured using the following environment variables:
- FABRIC_BASE_URL: The base URL of the running Fabric REST API server (fabric --serve).
- Default: http://127.0.0.1:8080
- FABRIC_API_KEY: The API key required to authenticate with the Fabric REST API server, if it's configured to require one.
- Default: None (Authentication is not attempted if not set).
- FABRIC_MCP_LOG_LEVEL: Sets the logging verbosity for the fabric-mcp server itself.
- Options: DEBUG, INFO, WARNING, ERROR, CRITICAL (case-insensitive).
- Default: INFO
You can set these variables in your shell environment (or put them into a .env file in the working directory) before running fabric-mcp:
export FABRIC_BASE_URL="http://your-fabric-host:port"
Claude Desktop / Cursor
Paste into your MCP client config file to install this server.
{
"mcpServers": {
"fabric mcp server": {
"fabric-mcp": {
"command": "uv",
"args": [
"sync",
"--dev"
]
}
}
}
}McpServers
{
"fabric-mcp": {
"command": "uv",
"args": [
"sync",
"--dev"
]
}
}[![License: MIT][mit_license]][mit_license_link]
| main | | | develop | |
|:---:|:---:|:---:|:---:|:---:|
| [![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] |
<div align="center">
<a href="https://mseep.ai/app/ksylvan-fabric-mcp">
</a>
</div>
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
- Fabric MCP Server
- Table of Contents
- What is this?
- Key Goals \& Features (Based on Design)
- How it Works
- Project Status
- Getting Started
- Prerequisites
- Installation From Source (for developers)
- Installation From PyPI (for users)
- Configuration (Environment Variables)
- Transport Options
- Contributing
- License
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
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 and available Fabric pattern templates via MCP's prompts/list mechanism.
3. When the user invokes a tool or prompt (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 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 feature-complete.
The project was completed by using the [BMAD-METHOD (Breakthrough Method of Agile Ai-Driven Development)][bmad-method].
The core architecture and proposed tools are outlined in the [High-Level Architecture Document][architecture_doc].
You can also use to explore the source code.
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) for developers
Installation From Source (for developers)
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!
Installation From PyPI (for users)
If you just want to use the fabric-mcp server without developing it, you can install it directly from PyPI:
```bash
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