Microsoft Fabric RTI
STDIOMCP server for Microsoft Fabric RTI enabling AI agents to interact with data services
MCP server for Microsoft Fabric RTI enabling AI agents to interact with data services
A Model Context Protocol (MCP) server implementation for Microsoft Fabric Real-Time Intelligence (RTI). This server enables AI agents to interact with Fabric RTI services by providing tools through the MCP interface, allowing for seamless data querying and analysis capabilities.
The Fabric RTI MCP Server creates a seamless integration between AI agents and Fabric RTI services through:
uvpowershell -ExecutionPolicy ByPass -c "irm https://astral.sh/uv/install.ps1 | iex"
or, check here for other install options
The Fabric RTI MCP Server is available on PyPI, so you can install it using pip. This is the easiest way to install the server.
1. Open the command palette (Ctrl+Shift+P) and run the command `MCP: Add Server`
2. Select install from Pip
3. When prompted, enter the package name `microsoft-fabric-rti-mcp`
4. Follow the prompts to install the package and add it to your settings.json file
The process should end with the below settings in your settings.json file.
{ "mcp": { "server": { "fabric-rti-mcp": { "command": "uvx", "args": [ "microsoft-fabric-rti-mcp" ], "env": { "KUSTO_SERVICE_URI": "https://cluster.westus.kusto.windows.net/", //optionally provide cluster URI "KUSTO_DATABASE": "Datasets" //optionally provide database } } } } }
pip install . or uv tool install .)settings.json file.{ "mcp": { "servers": { "kusto-mcp": { "command": "uv", "args": [ "--directory", "C:/path/to/fabric-rti-mcp/", "run", "-m", "fabric_rti_mcp.server" ], "env": { "KUSTO_SERVICE_URI": "https://cluster.westus.kusto.windows.net/", //optionally provide cluster URI "KUSTO_DATABASE": "Datasets" //optionally provide database } } } } }
Assuming you have python installed and the repo cloned:
pip install -e ".[dev]"
Add the server to your
{
"mcp": {
"servers": {
"local-fabric-rti-mcp": {
"command": "python",
"args": [
"-m",
"fabric_rti_mcp.server"
]
}
}
}
}
Use the Python: Attach configuration in your launch.json to attach to the running server.
Once VS Code picks up the server and starts it, navigate to it's output:
MCP: List Serverslocal-fabric-rti-mcp and select Show OutputPython: Attach configuration in your launch.json file, and paste the PID of the server in the promptThe MCP Server seamlessly integrates with your host operating system's authentication mechanisms, making it super easy to get started! We use Azure Identity under the hood via DefaultAzureCredential, which tries these credentials in order:
EnvironmentCredential) - Perfect for CI/CD pipelinesVisualStudioCredential) - Uses your Visual Studio credentialsAzureCliCredential) - Uses your existing Azure CLI loginAzurePowerShellCredential) - Uses your Az PowerShell loginAzureDeveloperCliCredential) - Uses your azd loginInteractiveBrowserCredential) - Falls back to browser-based login if neededIf you're already logged in through any of these methods, the Fabric RTI MCP Server will automatically use those credentials.
Your credentials are always handled securely through the official Azure Identity SDK - we never store or manage tokens directly.
MCP as a phenomenon is very novel and cutting-edge. As with all new technology standards, consider doing a security review to ensure any systems that integrate with MCP servers follow all regulations and standards your system is expected to adhere to. This includes not only the Azure MCP Server, but any MCP client/agent that you choose to implement down to the model provider.
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This project has adopted the Microsoft Open Source Code of Conduct. For more information see the Code of Conduct FAQ or contact [email protected] with any additional questions or comments.
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