fabric-mcp-server MCP Server

What does “fabric-mcp-server” MCP Server do?

The fabric-mcp-server is a Model Context Protocol (MCP) server designed to integrate Fabric patterns with Cline, exposing them as tools for AI-driven task execution. By acting as a bridge, it allows AI assistants to utilize structured Fabric patterns as callable tools, thereby enhancing development workflows. This integration enables tasks such as claim analysis, summarization, and wisdom extraction directly within supported platforms like Cline. The server leverages the standardized MCP interface to make these capabilities easily accessible, ultimately augmenting the AI’s power to interact with and manipulate complex information through reusable, pattern-based workflows.

List of Prompts

No explicit prompt templates are mentioned in the repository or documentation.

List of Resources

No specific MCP resources are documented or exposed by the fabric-mcp-server.

List of Tools

The fabric-mcp-server exposes Fabric patterns as tools. Examples include:

• analyze_claims: Analyzes claims within provided content.
• summarize: Generates summaries from input data or text.
• extract_wisdom: Extracts key insights or wisdom from documents.
• create_mermaid_visualization: Produces mermaid.js diagrams based on structured data.

Note: The full set of tools corresponds to the patterns available in the fabric/patterns directory.

Use Cases of this MCP Server

• Claim Analysis: Automatically analyze and validate claims within documents or datasets, streamlining research and due diligence.
• Summarization Services: Generate concise summaries of lengthy articles or reports, improving information digestion for developers and end-users.
• Insight Extraction: Extract actionable insights or distilled “wisdom” from large volumes of data, supporting knowledge management tasks.
• Visualization Generation: Create mermaid diagrams or other visualizations directly from structured data, aiding documentation and system design.
• Pattern-Based Task Automation: Leverage the full suite of Fabric patterns to automate repetitive or complex tasks within development workflows.

How to set it up

Windsurf

No setup instructions for Windsurf are provided in the repository.

Claude

No setup instructions for Claude are provided in the repository.

Cursor

No setup instructions for Cursor are provided in the repository.

Cline

1. Clone the Repository:
   Clone the fabric-mcp-server repository to your local system.
2. Install Dependencies:
   Navigate into the fabric-mcp-server directory and run npm install.
3. Build the Project:
   Run npm run build to compile the TypeScript code.
4. Edit Cline Settings File:
   Add the MCP server configuration to your Cline settings file.
   • Windows: C:\Users\<username>\AppData\Roaming\Code\User\globalStorage\saoudrizwan.claude-dev\settings\cline_mcp_settings.json
   • macOS: ~/Library/Application Support/Code/User/globalStorage/saoudrizwan.claude-dev/settings/cline_mcp_settings.json
   • Linux: ~/.config/Code/User/globalStorage/saoudrizwan.claude-dev/settings/cline_mcp_settings.json
5. Insert Configuration Example:

"fabric-mcp-server": {
  "command": "node",
  "args": [
    "<path-to-fabric-mcp-server>/build/index.js"
  ],
  "env": {},
  "disabled": false,
  "autoApprove": [],
  "transportType": "stdio",
  "timeout": 60
}

Replace <path-to-fabric-mcp-server> with your actual path.

Securing API Keys

You can secure API keys using environment variables in the config as follows:

"fabric-mcp-server": {
  "command": "node",
  "args": [
    "<path-to-fabric-mcp-server>/build/index.js"
  ],
  "env": {
    "API_KEY": "${env:MY_API_KEY}"
  },
  "inputs": {
    "api_key": "${env:MY_API_KEY}"
  },
  "disabled": false,
  "autoApprove": [],
  "transportType": "stdio",
  "timeout": 60
}

How to use this MCP inside flows

Using MCP in FlowHunt

To integrate MCP servers into your FlowHunt workflow, start by adding the MCP component to your flow and connecting it to your AI agent:

Click on the MCP component to open the configuration panel. In the system MCP configuration section, insert your MCP server details using this JSON format:

{
  "fabric-mcp-server": {
    "transport": "streamable_http",
    "url": "https://yourmcpserver.example/pathtothemcp/url"
  }
}

Once configured, the AI agent can use this MCP as a tool with access to all its functions and capabilities. Remember to change “fabric-mcp-server” to your preferred name and update the URL as appropriate.

Overview

Based on the available documentation, fabric-mcp-server provides a clear overview, setup instructions, and a list of exposed tools, but lacks detailed documentation for prompts, resources, and features like sampling or roots. It is functional for Cline integration but would benefit from broader platform support and richer documentation.

Our opinion

If you are looking to expose Fabric patterns as tools for AI-driven workflows, especially within Cline, this MCP server is a solid foundation. However, its documentation and feature set are somewhat limited compared to more mature MCP servers. The basic requirements for licensing and tool exposure are met, but the lack of prompt/resource samples and sampling/roots support keep it from a higher score.

MCP Score