Snowflake MCP Server

A read-only Model Context Protocol server for Snowflake, using SSO (browser-based) authentication.

Gives Claude (or any MCP client) the ability to explore your Snowflake account — list databases, schemas, tables, views, describe columns, and run SELECT queries.

Features

SSO authentication (opens browser on first connection, then reuses the session)
Connection pooling with 30-minute timeout
Query caching (5 min TTL)
Read-only safety: only SELECT / SHOW / DESCRIBE queries are allowed
Async query support for long-running queries
Paginated results
Multiple output formats (records, columns, split, CSV)

Quick start with uv

uv is the easiest way to run this without managing a virtualenv yourself.

1. Install uv (if you don't have it)

# macOS / Linux
curl -LsSf https://astral.sh/uv/install.sh | sh

# Windows (PowerShell)
powershell -ExecutionPolicy ByPass -c "irm https://astral.sh/uv/install.ps1 | iex"

2. Configure your Snowflake details

Set environment variables (recommended) or edit the defaults at the top of snowflake_mcp_server.py.

# Required
export SNOWFLAKE_ACCOUNT="your-account-id"       # e.g. "xy12345.eu-west-1"
export SNOWFLAKE_USER="your.email@company.com"

# Optional — defaults shown
export SNOWFLAKE_AUTHENTICATOR="externalbrowser"  # SSO via browser
export SNOWFLAKE_WAREHOUSE=""                     # uses account default if empty
export SNOWFLAKE_ROLE=""                          # uses account default if empty
export SNOWFLAKE_DATABASE=""                      # uses account default if empty
export SNOWFLAKE_SCHEMA=""                        # uses account default if empty

On Windows (PowerShell):

$env:SNOWFLAKE_ACCOUNT = "your-account-id"
$env:SNOWFLAKE_USER = "your.email@company.com"

3. Run the server

uv run --with snowflake-connector-python --with mcp snowflake_mcp_server.py

Or, if you prefer to create a project first:

uv init --no-package
uv add snowflake-connector-python mcp
uv run snowflake_mcp_server.py

4. Add to Claude Code

Option A — CLI (easiest):

claude mcp add --transport stdio snowflake \
  --env SNOWFLAKE_ACCOUNT=your-account-id \
  --env SNOWFLAKE_USER=your.email@company.com \
  --env SNOWFLAKE_ROLE=YOUR_ROLE \
  -- uv run --with snowflake-connector-python --with mcp /absolute/path/to/snowflake_mcp_server.py

Option B — manual config:

Add to .mcp.json in your project root (shared with team) or ~/.claude.json (personal):

{
  "mcpServers": {
    "snowflake": {
      "type": "stdio",
      "command": "uv",
      "args": [
        "run",
        "--with", "snowflake-connector-python",
        "--with", "mcp",
        "/absolute/path/to/snowflake_mcp_server.py"
      ],
      "env": {
        "SNOWFLAKE_ACCOUNT": "your-account-id",
        "SNOWFLAKE_USER": "your.email@company.com",
        "SNOWFLAKE_ROLE": "YOUR_ROLE"
      }
    }
  }
}

Verify it's registered with claude mcp list.

Available tools

Tool	Description
`test_connection`	Verify connectivity and get session info
`list_databases`	List all accessible databases
`list_schemas`	List schemas in a database
`list_tables`	List tables (optionally with row counts)
`list_views`	List views in a schema
`describe_table`	Get column-level schema for a table or view
`describe_query`	Preview output columns without running the full query
`read_data`	Run a SELECT query (up to 50k rows)
`read_data_paginated`	Paginated query results with total count
`read_data_pandas`	Results in records / columns / split / CSV format
`execute_async`	Submit a long-running query asynchronously
`get_query_status`	Check async query progress
`get_async_results`	Fetch results from a completed async query
`list_async_queries`	List all tracked async queries

Notes

The first connection opens your default browser for SSO login. Subsequent calls reuse the session.
All queries are validated to be read-only — DML and DDL are blocked.
Snowflake object names are case-sensitive when created with quotes. The server will automatically retry with quoted names if the unquoted version fails.

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