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Snowflake MCP Lambda

@Samar-Bons

Repository

Basic information

Category

Cloud & Infrastructure

License

GPL-3.0

Transports

stdio

Author

Samar-Bons

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

1

What LLM does the application use?

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

2

Are uploaded files stored permanently?

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

3

What authentication methods are supported?

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

4

What database is used for querying?

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

5

What file formats are supported?

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

Comments

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