"Snowflake" matching MCP servers:

Enables AI agents to execute SQL queries and explore Snowflake databases using natural language, with schema discovery, table inspection, and readonly mode.

Enables LLMs to directly query and interact with Snowflake databases, supporting SELECT queries, schema exploration, and table operations.

Provides read-only visibility into Snowflake warehouse cost and query performance, including credit usage by warehouse, expensive queries, and heuristic right-sizing recommendations.

Snowflake integration implementing read and (optional) write operations as well as insight tracking

Provides secure, read-only access to Snowflake data warehouses, enabling AI assistants to explore schemas, write queries, and analyze data with SSO authentication and smart caching.

Enables AI assistants to securely connect to Snowflake data warehouses and execute SQL queries through natural language interactions. Supports multiple authentication methods and provides formatted query results with built-in security controls.

Enables AI agents to connect to Snowflake Data Cloud and Cortex AI for executing SQL, managing warehouses, tables, pipelines, governance, and AI-powered operations through a comprehensive set of enterprise-grade tools.

An MCP server for Snowflake that enables table discovery, metadata inspection, and SQL query execution through natural language, supporting password, key-pair, and OAuth authentication.

A Snowflake MCP server — SQL queries, schema exploration, and data insights for AI assistants

Enables execution of Snowflake stored procedures through the MCP server by providing a framework to define, manage, and call procedures from specific schemas or individual procedures.

Provides AI assistants with tools to query, filter, and analyze JIRA issues stored in Snowflake, enabling natural language interaction with issue data.

Lets AI agents estimate the cost and result size of BigQuery and Snowflake queries before they run, then execute them only within per-call byte, row, and dollar bounds. Estimates are labeled with an accuracy tier so agents never over-trust an approximate figure.

Read-only, PII-masked MCP server for querying data federated through DuckDB — list sources/tables, inspect schemas, and run SQL across Postgres, MySQL, SQLite, Snowflake, BigQuery, Excel, and files, governed per source/table/column by a charter.yaml contract. Snowflake, BigQuery, Excel, and files, governed per source/table/column by a charter.yaml contract.

Answers natural-language questions against Postgres, Oracle, Snowflake, Databricks, DuckDB and SQLite via db_read(question) and get_schema(), each separately grantable. The model only proposes SQL — shape, access, dialect and query plan are checked deterministically before any rows are read, and the server refuses rather than returning an unverified answer.

Enables AI agents to discover, request access to, and query data products in Data Mesh Manager, enforcing governance policies while retrieving business data from platforms like Snowflake and Databricks.

Natural language to SQL engine with multi-connector support (PostgreSQL, MySQL, Snowflake, BigQuery, DuckDB), document QA, semantic caching, and self-hosted MCP server.

Zero-config data quality monitoring as MCP tools. Profiles a warehouse (Postgres, BigQuery, Snowflake, MySQL, DuckDB), detects anomalies, and gates CI — read-only with the connection resolved server-side, never via the model.

7 SQL tools (validate, format, parse, lint, security scan, metadata extraction, full analysis) over Streamable HTTP. Supports 6 SQL dialects with 1.25M+ ops/sec.

MCP server for Durable energy trading analytics that connects Claude Desktop to Snowflake data, enabling natural-language queries about spreads, constraints, generators, and prices.

Exposes SQL Server and Snowflake schema metadata to AI coding agents, enabling schema search, join path discovery, and stored procedure metadata retrieval without live queries.