Run Krusgraph locally

The hosted connector at https://krusgraph.com/mcp reads Fabric through your Microsoft sign-in. The same engine also runs on your own machine as a small MCP server over one ODBC connection: against a SQL Server you reach with Windows sign-in, an Azure SQL database, or a Fabric SQL analytics endpoint. Nothing leaves your machine except the queries to that database; there is no account with us and no sign-in with Microsoft. Keys and relationships are inferred the same way, with the same rules (K0–K5, R0–R4), and the drafts are written to your disk.

Status: the local server is unit-tested against recorded SQL Server metadata (declared keys, several schemas, names with spaces, nvarchar(max), datetimeoffset, uniqueidentifier, computed columns, views) and against a Fabric SQL analytics endpoint in the test suite; it has not yet been run against a live SQL Server by us.

What you need

Python 3.10 or later.
Microsoft ODBC Driver 18 for SQL Server (the local server speaks ODBC).
A database sign-in that can read metadata and run counting queries. Give it db_datareader and nothing more; create_views is the only tool that writes, and it is a dry run until you say otherwise.

Install

pip install "fabric-backend[mcp] @ git+https://github.com/Krusmaster/fabric-backend"

This installs fabric-backend-mcp (the local MCP server) and fabric-backend (the command line).

Claude Desktop

Add the server to Claude Desktop's configuration (Settings → Developer → Edit config), with your own server and database. Windows sign-in needs no password in the string:

{
  "mcpServers": {
    "krusgraph-local": {
      "command": "fabric-backend-mcp",
      "env": {
        "FABRIC_ODBC": "Driver={ODBC Driver 18 for SQL Server};Server=sql01;Database=shopdb;Trusted_Connection=yes;TrustServerCertificate=yes",
        "FABRIC_SOURCE_KIND": "SQLServer"
      }
    }
  }
}

If Windows cannot find fabric-backend-mcp, use the full path of the executable in your Python Scripts folder. TrustServerCertificate=yes accepts the server's own certificate; drop it when the server has one your machine trusts. For a Fabric endpoint instead, use Server=<sql analytics endpoint host>;Database=<lakehouse>;Authentication=ActiveDirectoryInteractive and FABRIC_SOURCE_KIND=Lakehouse (or Warehouse, SQLDatabase).

Restart Claude Desktop and ask, for example, "Plan the tables in schema dbo" or "Check the plan against the data".

What works locally

list_tables, plan_backend, validate_plan, sample_rows, join_path, data_dictionary, dbt_schema, analyze_schema, suggest_relationships, rayfin_connector, create_views (dry run by default; CREATE OR ALTER VIEW in a schema of its own, never DROP), answer_query_draft (a question as T-SQL over the safest join path; runs nothing) and, for a lakehouse, silver_notebook_draft (the notebook that builds silver tables; nothing created). On SQL Server, declared primary and foreign keys are read and become K0 and R0; relationships into another schema are left out rather than guessed.

Two drafts are Fabric-specific and answer with an explanation when FABRIC_SOURCE_KIND is SQLServer: the Power BI semantic model in Direct Lake mode (it needs a Fabric SQL analytics endpoint; open the tables in Power BI Desktop instead, or mirror the database into Fabric) and the Fabric IQ ontology. The tools that need Fabric item ids (create_backend, describe_backend, plan_drift, app_starter, app_readiness, create_semantic_model, create_data_agent, lineage, create_silver_notebook and the drift monitor) live in the hosted connector only.

The 20-minute demo

fabric-backend demo opens one local page on 127.0.0.1: connect a SQL Server over ODBC or a folder of CSV / Excel files (or try the synthetic shop schema), see the map with the rule behind every connection, check the plan against the rows with Accept and Skip on each suggestion, and get five questions answered on the data with the SQL shown, plus the exports (data dictionary, dbt tests, a Lovable prompt). No account, no cloud. The script for running it with a customer is in the repository: docs/DEMO-20-MIN.md.

Without any connection: the CSV path

Export the columns once (INFORMATION_SCHEMA.COLUMNS of one schema as CSV, with the columns table_schema, table_name, column_name, data_type, ordinal_position) and run the command line on the file:

fabric-backend plan columns.csv --schema dbo --mermaid er.mmd
fabric-backend dictionary columns.csv --schema dbo --out dictionary.md
fabric-backend dbt columns.csv --schema dbo --out schema.yml
fabric-backend analyze columns.csv --schema dbo
fabric-backend views columns.csv --schema dbo

The plan, the diagram, the data dictionary, the dbt sources file and the view SQL come from the names alone, with the rule per key and relationship; the counting checks need a connection.

Source

Open source under the MIT licence: https://github.com/Krusmaster/fabric-backend. The hosted connector and its docs: /docs.