Snowflake MCP Server

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Overview

A Model Context Protocol (MCP) server implementation that provides database interaction with Snowflake. This server enables running SQL queries via tools and exposes data insights and schema context as resources.

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Installing Locally

1. Install uv:

uv is a modern package manager for Python that will handle your python environment and dependencies.

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

2. Install Python 3.12 (if not already installed):

uv venv --python=3.12

3. Set up Cursor/Windsurf access. If you need access, request it using the /request-access command in Slack.

4. Ensure your Snowflake access is active. Use the /snowflake-access command in Slack for activation.

Note - if you had a Snowflake account but get a SAML error when logging in, run /snowflake-access re_enable on Slack to automatically re-enable your account

5. Open the project in your IDE and configure the MCP server

Cursor

Open the project in Cursor and create a directory named .cursor in the root of the project.

Create a file named mcp.json inside the .cursor directory and paste the following JSON configuration, updating the values as below.

Windsurf

Create a file named mcp_config.json in the root of the project and paste the following JSON configuration, updating the values as below.

{
    "mcpServers": {
        "snowflake_local": {
            "command": "/Users/<macusername>/.local/bin/uv", // Replace with your actual uv path. Run `which uv` in the terminal if you don't know this
            "args": [
                "--directory",
                "/Users/macusername/projects/mcp-snowflake-server",  // Replace with your actual repo path
                "run",
                "mcp_snowflake_server",
                "--account",
                "YW26239-DELIVEROO",
                "--warehouse",
                "bi_development",
                "--user",
                "first.last@deliveroo.com",  // Replace with your email address
                "--database",
                "production",
                "--schema",
                "core",
                "--authenticator",
                "externalbrowser"
            ]
        }
    }
}

6. Test the setup by typing "list databases" in the Cursor chat. This should display all available databases. Note that a browser window will open for authentication.

Troubleshooting

1. If the setup doesn't work, try restarting Cursor.
2. If you encounter a "no user found" error, you may need to activate your Snowflake account or create a new account.

Netskope SSL issues

The Netskope certificate file is included in this repository at certs/nscacert.pem and is automatically loaded by the server. If you need to update it, download a new pem file from this guide Netskope / SSL issues with python hitting APIs locally and update the file.

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Components

Resources

• memo://insights
  A continuously updated memo aggregating discovered data insights.
  Updated automatically when new insights are appended via the append_insight tool.

• context://table/{table_name}
  (If prefetch enabled) Per-table schema summaries, including columns and comments, exposed as individual resources.

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Tools

The server exposes the following tools:

Query Tools

• read_query
  Execute SELECT queries to read data from the database.
  Input:

  • query (string): The SELECT SQL query to execute
    Returns: Query results as array of objects

• write_query (enabled only with --allow-write)
  Execute INSERT, UPDATE, or DELETE queries.
  Input:

  • query (string): The SQL modification query
    Returns: Number of affected rows or confirmation

• create_table (enabled only with --allow-write)
  Create new tables in the database.
  Input:

  • query (string): CREATE TABLE SQL statement
    Returns: Confirmation of table creation

Schema Tools

• list_databases
  List all databases in the Snowflake instance.
  Returns: Array of database names

• list_schemas
  List all schemas within a specific database.
  Input:

  • database (string): Name of the database
    Returns: Array of schema names

• list_tables
  List all tables within a specific database and schema.
  Input:

  • database (string): Name of the database
  • schema (string): Name of the schema
    Returns: Array of table metadata

• describe_table
  View column information for a specific table.
  Input:

  • table_name (string): Fully qualified table name (database.schema.table)
    Returns: Array of column definitions with names, types, nullability, defaults, and comments

Analysis Tools

• append_insight
  Add new data insights to the memo resource.
  Input:
  • insight (string): Data insight discovered from analysis
    Returns: Confirmation of insight addition
    Effect: Triggers update of memo://insights resource

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License

MIT