
Microsoft Fabric MCP Server
The Microsoft Fabric MCP Server enables seamless AI-driven interaction with Microsoft Fabric's data engineering and analytics ecosystem. It supports workspace m...
Expose Fabric patterns as powerful, reusable AI tools for claim analysis, summarization, insight extraction, and visualization in your development workflows.
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.
No explicit prompt templates are mentioned in the repository or documentation.
No specific MCP resources are documented or exposed by the fabric-mcp-server.
The fabric-mcp-server exposes Fabric patterns as tools. Examples include:
Note: The full set of tools corresponds to the patterns available in the fabric/patterns
directory.
No setup instructions for Windsurf are provided in the repository.
No setup instructions for Claude are provided in the repository.
No setup instructions for Cursor are provided in the repository.
fabric-mcp-server
repository to your local system.fabric-mcp-server
directory and run npm install
.npm run build
to compile the TypeScript code.C:\Users\<username>\AppData\Roaming\Code\User\globalStorage\saoudrizwan.claude-dev\settings\cline_mcp_settings.json
~/Library/Application Support/Code/User/globalStorage/saoudrizwan.claude-dev/settings/cline_mcp_settings.json
~/.config/Code/User/globalStorage/saoudrizwan.claude-dev/settings/cline_mcp_settings.json
"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.
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
}
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.
Section | Availability | Details/Notes |
---|---|---|
Overview | ✅ | Overview and features found in README |
List of Prompts | ⛔ | No explicit prompt templates documented |
List of Resources | ⛔ | No specific resources mentioned |
List of Tools | ✅ | Several tools (patterns) listed |
Securing API Keys | ✅ | Example with env variables in README |
Sampling Support (less important in evaluation) | ⛔ | Not mentioned |
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.
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.
Has a LICENSE | ✅ (MIT) |
---|---|
Has at least one tool | ✅ |
Number of Forks | 1 |
Number of Stars | 5 |
fabric-mcp-server is a Model Context Protocol (MCP) server that exposes Fabric patterns as tools, enabling AI assistants to perform claim analysis, summarization, wisdom extraction, and diagram generation within platforms like Cline and FlowHunt.
It exposes all available Fabric patterns as tools, including analyze_claims, summarize, extract_wisdom, and create_mermaid_visualization. The full set corresponds to the patterns available in the fabric/patterns directory.
Clone the repository, install dependencies, build the project, and add the provided MCP server configuration to your Cline settings file. Use environment variables for any API keys for security.
Yes, you can add the MCP component in FlowHunt and configure it with your fabric-mcp-server details, allowing your flows and AI agents to use all exposed tools.
Typical use cases include claim analysis for research, summarization of long texts, extraction of actionable insights, and automated diagram generation from structured data.
Supercharge your AI workflows by connecting fabric-mcp-server to FlowHunt or Cline. Automate claim analysis, summarization, and more using reusable Fabric patterns.
The Microsoft Fabric MCP Server enables seamless AI-driven interaction with Microsoft Fabric's data engineering and analytics ecosystem. It supports workspace m...
The Fibery MCP Server bridges your Fibery workspace with AI assistants using the Model Context Protocol, enabling natural language access to databases, metadata...
The Model Context Protocol (MCP) Server bridges AI assistants with external data sources, APIs, and services, enabling streamlined integration of complex workfl...