Snowflake MCP Lambda

@Samar-Bons

Repository

Basic information

About Snowflake MCP Lambda

A remote MCP Server for Snowflake. Deployed as a AWS Lambda Function

#snowflake #aws-lambda #mcp-server

Snowflake MCP Lambda config

No standard config provided

This server doesn't expose a parseable MCP config block in its README. See the repository for install instructions.

Repository

Snowflake MCP Lambda tools

Tool list not available yet

This listing does not currently have a tool list to display. This does not mean the server has no tools; check its documentation for supported capabilities.

We auto-extract tools from the README. The maintainer can list them under a ## Tools heading to populate this section.

Overview

What is snowflake-mcp-lambda?

A web application that enables non-technical users to upload CSV files and query them using natural language via a chat interface. It integrates with Google Gemini for natural language to SQL conversion and optionally uses Google OAuth for authentication.

How to use snowflake-mcp-lambda?

Clone the repository, run make setup to copy the environment file, edit .env with your Gemini API key, then run make dev-setup to start containers. Access the frontend at http://localhost:3000 and the backend API at http://localhost:8000. Use make test to run backend tests.

Key features of snowflake-mcp-lambda

CSV upload with drag-and-drop and progress indication
Schema preview with auto-detected columns and data types
Natural language chat interface for querying data
SQL generation via Google Gemini LLM
Read-only queries (SELECT only) for security
No data persistence; files deleted after session ends
User-provided Gemini API key, never stored server-side
Optional Google OAuth authentication

Use cases of snowflake-mcp-lambda

Non-technical users analyzing CSV data without SQL knowledge
Quick data exploration and ad-hoc queries via chat
Prototyping data analysis workflows without database setup
Secure data analysis with temporary file handling

Frequently asked questions

What LLM does the application use?

It uses Google Gemini API for converting natural language questions to SQLite queries.

Are uploaded files stored permanently?

No, files are deleted after the session ends. The application does not persist data.

What authentication methods are supported?

Optional Google OAuth authentication is supported. Without it, users can still use the application.

What database is used for querying?

Uploaded CSV files are converted to in-memory SQLite databases for fast query execution.

What file formats are supported?

Currently only CSV files are supported. Future plans include Excel, JSON, and Parquet.

Comments

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