sql-server-mcp

A local MCP server for read-only SQL Server / MS Fabric queries. Runs as a single-user process over stdio, authenticated as you (delegated Entra ID identity, not a service principal) — one server process per tenant.

Prerequisites

• Python 3.14+ and uv

• Microsoft ODBC Driver 18 for SQL Server installed on your machine

• Either the Azure CLI logged in to the target tenant (az login --tenant <tenant-id>), or just a browser available — the server falls back to an interactive browser login if there's no CLI session

• An Entra ID account in the target tenant with read access to the target database (e.g. db_datareader)

Configuration

Each tenant gets its own set of environment variables:

Running it

The server is a uvx-runnable console script (sql-server-mcp), no manual install step needed - uvx builds an isolated environment for it on first run and reuses it afterwards. This repo isn't published anywhere yet, so point --from at a local checkout or a git URL:

uvx --from /path/to/sql-server-mcp sql-server-mcp

Once published to PyPI (or a private index), this simplifies to uvx sql-server-mcp directly, with no --from needed.

On startup it checks the ODBC driver is installed, then acquires a token (via az login if available, otherwise an interactive browser prompt) and runs over stdio.

Example MCP client config

One block per tenant:

{
  "mcpServers": {
    "tenant-a-sql": {
      "command": "uvx",
      "args": ["--from", "/path/to/sql-server-mcp", "sql-server-mcp"],
      "env": {
        "MCP_SQL_SERVER_NAME": "tenant-a.datawarehouse.fabric.microsoft.com",
        "MCP_SQL_DATABASE_NAME": "wh_silver",
        "MCP_AZURE_TENANT_ID": "<tenant-a-tenant-id>"
      }
    },
    "tenant-b-sql": {
      "command": "uvx",
      "args": ["--from", "/path/to/sql-server-mcp", "sql-server-mcp"],
      "env": {
        "MCP_SQL_SERVER_NAME": "tenant-b.datawarehouse.fabric.microsoft.com",
        "MCP_SQL_DATABASE_NAME": "wh_gold",
        "MCP_AZURE_TENANT_ID": "<tenant-b-tenant-id>"
      }
    }
  }
}

Tools

• query(sql) — run a single read-only SELECT. Table references must be fully qualified as database.schema.table; there's no mutable "active database". Returns at most MCP_MAX_ROWS rows, as a Markdown table.

• list_databases() — list user databases with status/metadata.

• search_schema(target, name, databases?, schema?, use_wildcard?) — search table or column metadata via INFORMATION_SCHEMA across one or more databases (all of them if databases is omitted).

• export_query(sql, path, format?) — run a single read-only SELECT and write the full, uncapped result to a local Parquet or CSV file instead of returning it. For large results or when the output feeds into other local tools (pandas, DuckDB, etc.).

Development

uv run pytest          # unit tests, no network/DB/Azure required
uv run python scripts/manual_smoke_test.py   # real end-to-end check against a live database