Snowflake Managed Server
FreeNot checkedEnables AI agents to securely query governed Snowflake data and use Cortex AI tools like semantic search and LLM completions, with read-only, allow-listed acces
About
Enables AI agents to securely query governed Snowflake data and use Cortex AI tools like semantic search and LLM completions, with read-only, allow-listed access and dual transport options (stdio and authenticated SSE).
README
A lean, security-first MCP server that exposes governed Snowflake data and Cortex AI to AI agents (e.g. agents built in Google Gemini Enterprise). See CLAUDE.md for the full architecture and security model.
Read-only by design. No DDL/DML. Access is restricted to an allow-list of governed views and enforced again by a least-privilege Snowflake role.
Tools
| Tool | What it does |
|---|---|
list_datasets |
List allow-listed governed datasets agents may query |
describe_dataset |
Column names/types for one allow-listed dataset |
run_query |
Run a single read-only SELECT/WITH query (row-capped, timed out) |
cortex_search |
Semantic retrieval over an approved Cortex Search service |
cortex_complete |
LLM completion via SNOWFLAKE.CORTEX.COMPLETE (allow-listed models) |
Cortex Analyst (NL→SQL) is a planned addition; it uses a REST endpoint and was left out of v0.1 to avoid shipping an untested HTTP client. The SQL-based Cortex tools above cover retrieval and completion today.
Transports
- stdio — stdin/stdout for a trusted local agent process (no network auth).
- sse — HTTP + Server-Sent Events, protected by bearer-token authentication
(
MCP_SSE_BEARER_TOKENS). Refuses to start if no tokens are configured.
Setup
python -m venv .venv && source .venv/bin/activate
pip install -e .
cp .env.example .env # then fill in Snowflake creds, allow-list, and limits
Run
# Local trusted agent (stdio)
python -m snowflake_mcp --transport stdio
# Network agents (authenticated SSE)
python -m snowflake_mcp --transport sse
# Agents connect to http://$MCP_SSE_HOST:$MCP_SSE_PORT/sse with header:
# Authorization: Bearer <one of MCP_SSE_BEARER_TOKENS>
Run with Docker (primary deployment)
The server is operated as a container service over SSE. Secrets are injected at
runtime from .env — never baked into the image.
cp .env.example .env # fill in creds, MCP_SSE_BEARER_TOKENS, and MCP_BEARER_TOKEN
docker compose up --build server # start the MCP server (SSE on 127.0.0.1:8080)
docker compose run --rm client # run the example client against it
The image runs as a non-root user and publishes only to 127.0.0.1. In
production, place the SSE port behind a gateway enforcing mTLS/OAuth.
Test
pip install pytest
pytest # all guardrail tests
pytest tests/test_security.py::test_rejects_dangerous_sql # a single test
Security notes
- Set
MCP_ALLOWED_OBJECTSto fully-qualified governed views only — an empty allow-list means the server can read nothing (safe default). - Prefer Snowflake key-pair auth; password is a dev-only fallback.
- The Snowflake role in
SNOWFLAKE_ROLEmust be least-privilege (read-only on governed views). MCP guardrails are defense-in-depth, not a substitute for it.
Installing Snowflake Managed Server
This server has no published package — it is built from source. Open the repository and follow its README.
▸ github.com/bitlabsdevteam/SnowFlask_managed_MCP_SERVERFAQ
Is Snowflake Managed Server MCP free?
Yes, Snowflake Managed Server MCP is free — one-click install via Unyly at no cost.
Does Snowflake Managed Server need an API key?
No, Snowflake Managed Server runs without API keys or environment variables.
Is Snowflake Managed Server hosted or self-hosted?
Self-hosted: the server runs locally on your machine via the install command above.
How do I install Snowflake Managed Server in Claude Desktop, Claude Code or Cursor?
Open Snowflake Managed Server on unyly.org, pick your client tab (Claude Desktop, Claude Code, Cursor) and press Install — the config is generated automatically, no JSON editing.
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