Metadata-Version: 2.4
Name: fabric-mcp
Version: 0.2.2
Summary: MCP Server for Fabric AI Framework
Project-URL: Homepage, https://github.com/ksylvan/fabric-mcp
Project-URL: Repository, https://github.com/ksylvan/fabric-mcp
Project-URL: Bug Tracker, https://github.com/ksylvan/fabric-mcp/issues
Project-URL: Documentation, https://github.com/ksylvan/fabric-mcp#readme
Author-email: Kayvan Sylvan <kayvan@sylvan.com>
License: MIT
License-File: LICENSE
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python :: 3
Requires-Python: >=3.10
Requires-Dist: modelcontextprotocol>=0.1.0
Requires-Dist: requests>=2.28.0
Description-Content-Type: text/markdown

# 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][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!

## 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](./docs/design.md).

**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](https://github.com/astral-sh/uv) (Python package and environment manager)

### Installation

1. **Clone the repository:**

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

2. **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.

3. **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!

## Contributing

Feedback on the [design document](./docs/design.md) 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](kayvan@sylvan.com) Licensed under the [MIT License](./LICENSE).

[fabricGithubLink]: https://github.com/danielmiessler/fabric
[MCP]: https://modelcontextprotocol.io/
