Snowflake
Summary
This MCP server, developed by Isaac Wasserman, enables AI assistants like Claude to interact with Snowflake databases. Built in Python, it provides capabilities for executing SQL queries, managing database schema, and maintaining a dynamic insights memo. The implementation stands out by offering a standardized interface to Snowflake's powerful data warehousing capabilities, allowing AI systems to analyze and manipulate large-scale datasets. By connecting AI capabilities with enterprise data infrastructure, this server enables AI assistants to perform complex data analysis, generate insights, and interact with business intelligence workflows. It is particularly useful for data exploration tasks, automated reporting, and building AI-powered analytics tools that can leverage the full power of Snowflake's cloud data platform.
Available Actions(8)
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
Execute `INSERT`, `UPDATE`, or `DELETE` queries. Input: query (string): The SQL modification query. Returns: Number of affected rows or confirmation.
create_table
Create new tables in the database. Input: query (string): `CREATE TABLE` SQL statement. Returns: Confirmation of table creation.
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.
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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