Install
$ agentstack add mcp-ksylvan-fabric-mcp ✓ scanned · ✓ verified, works with Claude Code, Cursor, and more.
Security review
✓ PassedNo issues found. Passed automated security review. · v0.1.0 How review works →
- ✓ Prompt-injection patterns
- ✓ Secret / credential exfiltration
- ✓ Dangerous shell & filesystem operations
- ✓ Untrusted network calls
- ✓ Known-malicious package signatures
What it can access
- ✓ Network access No
- ✓ Filesystem access No
- ✓ Shell / process execution No
- ● Environment & secrets Used
- ✓ Dynamic code execution No
From automated source analysis of v0.1.0. “Used” means the capability is present in the source — more access means more to trust, not that it’s unsafe.
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Reliability & compatibility
Declared compatibility
Compatibility is declared by the source manifest. End-to-end runtime verification is coming, see below.
We're building live execution health for every listing: tool-call success rate, median latency, uptime, and last-checked timestamps, measured, not self-reported. It isn't live yet, so we don't show numbers we can't stand behind.
How agent discovery & health will work →About
Fabric MCP Server
[![License: MIT][mitlicense]][mitlicense_link] [](https://pypi.python.org/pypi/fabric-mcp) [](https://deepwiki.com/ksylvan/fabric-mcp) [](https://mseep.ai/app/fca21713-c65c-42ee-b8fa-ce209466e70a)
| main | | | develop | | |:---:|:---:|:---:|:---:|:---:| | [![Main Tests][maintests]][maintestslink] | [![Main Publish][mainpublish]][mainpublishlink] | | [![Develop Tests][developtests]][developtestslink] | [![Develop Publish][developpublish]][developpublishlink] |
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](#fabric-mcp-server)
- [Table of Contents](#table-of-contents)
- [What is this?](#what-is-this)
- [Key Goals \& Features (Based on Design)](#key-goals--features-based-on-design)
- [How it Works](#how-it-works)
- [Project Status](#project-status)
- [Getting Started](#getting-started)
- [Prerequisites](#prerequisites)
- [Installation From Source (for developers)](#installation-from-source-for-developers)
- [Installation From PyPI (for users)](#installation-from-pypi-for-users)
- [Configuration (Environment Variables)](#configuration-environment-variables)
- [Transport Options](#transport-options)
- [Contributing](#contributing)
- [License](#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
- An MCP Host (e.g., an IDE extension) connects to this Fabric MCP Server.
- The Host discovers available tools (like
fabric_run_pattern) via MCP'slist_tools()mechanism and available Fabric pattern templates via MCP'sprompts/listmechanism. - 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.
- The Fabric MCP Server translates the MCP request into a corresponding REST API call to a running
fabric --serveinstance. - The
fabric --serveinstance executes the pattern. - 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 [](https://deepwiki.com/ksylvan/fabric-mcp) 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)
- Clone the repository:
``bash git clone https://github.com/ksylvan/fabric-mcp.git cd fabric-mcp ``
- Install dependencies using uv sync:
``bash 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.
- Activate the virtual environment (uv will create it if needed):
- On macOS/Linux:
``bash source .venv/bin/activate ``
- On Windows:
``bash .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:
# Using pip
pip install fabric-mcp
# Or using uv
uv pip install fabric-mcp
This will install the package and its dependencies. You can then run the server using the fabric-mcp command.
Configuration (Environment Variables)
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 thefabric-mcpserver 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"
# This must match the key used by fabric --serve
export FABRIC_API_KEY="your_secret_api_key"
export FABRIC_MCP_LOG_LEVEL="DEBUG"
# Standard I/O transport (default)
fabric-mcp --stdio
# HTTP Streamable transport for HTTP-based MCP clients
fabric-mcp --http-streamable
# Custom host/port for HTTP transport
fabric-mcp --http-streamable --host 0.0.0.0 --port 3000 --mcp-path /message
Transport Options
The fabric-mcp server supports multiple transport methods:
--stdio: Standard I/O transport for direct MCP client integration (default)--http-streamable: HTTP-based transport that runs a full HTTP server for MCP communication--host: Server bind address (default: 127.0.0.1)--port: Server port (default: 8000)--mcp-path: MCP endpoint path (default: /message)
For more details on transport configuration, see the [Infrastructure and Deployment Overview](./docs/architecture/infrastructure-and-deployment-overview.md#transport-configuration).
Contributing
Read the [contribution document here](./docs/contributing.md) and please follow the guidelines for this repository.
Also refer to the [cheat-sheet for contributors](./docs/contributing-cheatsheet.md) which contains a micro-summary of the development workflow.
License
Copyright (c) 2025, [Kayvan Sylvan](kayvan@sylvan.com) Licensed under the [MIT License](./LICENSE).
[fabricGithubLink]: https://github.com/danielmiessler/fabric [MCP]: https://modelcontextprotocol.io/ [architecturedoc]: ./docs/architecture/index.md [developpublishlink]: https://github.com/ksylvan/fabric-mcp/actions/workflows/publish.yml?branch=develop [developpublish]: https://github.com/ksylvan/fabric-mcp/actions/workflows/publish.yml/badge.svg?branch=develop [developtestslink]: https://github.com/ksylvan/fabric-mcp/actions/workflows/tests.yml?branch=develop [developtests]: https://github.com/ksylvan/fabric-mcp/actions/workflows/tests.yml/badge.svg?branch=develop [mitlicenselink]: https://opensource.org/licenses/MIT [mitlicense]: https://img.shields.io/badge/License-MIT-yellow.svg [mainpublishlink]: https://github.com/ksylvan/fabric-mcp/actions/workflows/publish.yml [mainpublish]: https://github.com/ksylvan/fabric-mcp/actions/workflows/publish.yml/badge.svg [maintestslink]: https://github.com/ksylvan/fabric-mcp/actions/workflows/tests.yml [maintests]: https://github.com/ksylvan/fabric-mcp/actions/workflows/tests.yml/badge.svg [bmad-method]: https://github.com/bmadcode/BMAD-METHOD
Source & license
This open-source MCP server is cataloged on AgentStack and links to its original source — we do not rehost the code.
- Author: ksylvan
- Source: ksylvan/fabric-mcp
- License: MIT
Install and usage instructions live in the source repository linked above.
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Versions
- v0.1.0 Imported from the upstream source.