snowflake · MCP Server

Improvado MCP — Talk to Your Snowflake Warehouse Naturally

Improvado's MCP server connects Snowflake to Claude, Cursor, and other AI agents. Query your Snowflake data in natural language — no manual exports or API scripts required.

46K+ metrics ·Read & Write access ·500+ platforms ·<60s setup

Read

Read: Instant Answers from Snowflake

Stop paying data vendors to rent access to your own data. Ask your AI agent to query Snowflake tables directly — aggregating across 150+ schemas, joining datasets that used to require a full data engineering sprint — in plain language.

Example prompts

"What are the top 10 campaigns by attributed revenue in the marketing_performance schema for Q1 2026?"

2 hrs → 2 min

"How much did we spend on Snowflake compute credits last month? Break it down by warehouse and user."

30 min → 45 sec

"Show me data discrepancies between the raw_events table and the aggregated_sessions view for last Tuesday. Where do the row counts diverge?"

3 hrs → 5 min

Works with Claude ChatGPT Cursor +5

Write

Write: Automate Snowflake Actions

Create tables, run transformations, manage warehouse sizes, and schedule queries — all through natural language. Let your AI agent handle Snowflake administration and pipeline operations without writing SQL by hand.

Example prompts

"Create a new table in the reporting schema: campaign_summary with columns campaign_id, spend, impressions, clicks, conversions. Populate it from the raw_campaigns table for the last 90 days."

45 min → 3 min

"Resize the ANALYTICS_WH warehouse from X-Large to Medium during off-peak hours (10pm–6am) to reduce compute costs."

20 min → 1 min

"Run the dbt model `fct_revenue_attribution` in the production environment and send the row count and any errors to #data-team in Slack."

30 min → 2 min

Every action logged · Fully reversible · SOC 2 certified

Monitor

Monitor: Catch Snowflake Issues Before They Escalate

Track pipeline freshness, query costs, and data quality automatically. Get AI-powered alerts when tables go stale, compute costs spike, or row count anomalies signal a broken upstream feed.

Example prompts

"Alert me if any table in the marketing schema hasn't been updated in more than 26 hours — possible pipeline failure."

Manual → auto

"Daily: send a Snowflake cost summary — total credits consumed yesterday, top 5 most expensive queries, and any warehouse that ran for more than 4 hours continuously."

1 hr → auto

"Flag if the row count in fct_ad_spend drops by more than 10% compared to the same day last week."

Manual → auto

Alerts sent to Slack, email, or your AI agent

Full cycle

The Closed Loop: Read → Decide → Write → Monitor

Your AI agent doesn't just surface data — it acts. Adjust pricing, update product descriptions, manage inventory, apply discounts — all through natural language. The MCP server translates intent into API operations.

Every phase runs through the same MCP connection. One protocol, all platforms, full governance. No switching between tools.

Ideate

Launch

Measure

Analyze

Report

Iterate

One conversation. All six phases. Every platform.

The daily grind

Common problems. Direct answers.

Challenge 1

Paying Vendors to Rent Access to Your Own Data

The problem

Teams that have built Snowflake as their central data warehouse still pay third-party vendors for dashboards and reports that query data already sitting in their own warehouse. The vendor abstracts the underlying SQL behind a GUI, charges for the access layer, and returns a view that's less flexible than the source. Teams end up paying twice — for storage and for someone else to read it.

How MCP solves it

Improvado MCP connects your AI agent directly to Snowflake. Instead of routing through an expensive intermediary, your agent writes and executes the query against your own warehouse. You own the data and the compute. The agent is the access layer — not another vendor.

Try asking

Query our Snowflake warehouse for the last 90 days of cross-channel attribution data and give me a breakdown by channel, campaign, and business unit.

Answer in seconds

All data sources, one query

Challenge 2

Full-Overwrite API Syncs Are Creating Massive Compute Overages

The problem

Many data pipelines sync to Snowflake by deleting and rewriting entire tables on every run. For large tables, this creates compute and storage costs that compound daily — a table with 10M rows being fully overwritten every 6 hours burns far more credits than an incremental update. The overage appears at end of month with no obvious cause in the Snowflake billing report.

How MCP solves it

Ask your AI agent to audit your Snowflake warehouse for full-overwrite patterns, identify the most expensive tables by daily compute cost, and flag pipelines that could be rewritten as incremental loads — before next month's bill arrives.

Try asking

Which tables in our Snowflake warehouse have the highest write frequency and largest data volume? Show compute costs per table and flag any that look like they're doing full overwrites on every sync.

Full detail preserved

No data loss on export

Challenge 3

Connecting 150 SQL Servers Into One Queryable View Is a Multi-Sprint Project

The problem

Enterprise data teams often manage Snowflake environments with dozens of schemas, hundreds of tables, and upstream feeds from 150+ source systems. Answering a cross-schema question that would take one business analyst 30 seconds to formulate requires an engineer to understand the schema relationships, write the join logic, and validate the output — a half-day process for a question that recurs weekly.

How MCP solves it

Improvado MCP gives your AI agent schema-level awareness of your entire Snowflake environment. The agent can discover table relationships, write multi-schema JOINs, and return a clean answer — without requiring the requester to know which schema holds which data.

Try asking

Join the customer_accounts table in the CRM schema with ad_spend data in the marketing schema and LTV data in the finance schema. Give me a revenue-per-customer breakdown by acquisition channel for our top 20 accounts.

Unified data model

Compare anything side by side

👥 Teams

One Framework. Five Roles. Zero Setup.

Same MCP connection, different workflows for every team member. Each role asks in natural language — the MCP server handles the complexity (rate limits, auth, schema normalization, governance) behind the scenes.

Agency CEO

Portfolio health. Client risk. Revenue signals.

Media Strategist

70% strategy, not 70% ops. Auto campaign QA.

Marketing Analyst

Zero wrangling. Cross-platform. AI narratives.

Account Manager

QBR decks auto-generated. Call prep in 30s.

Creative Director

Performance-to-brief. Predict winners before spend.

FAQ

Common questions

What is Snowflake MCP?

Snowflake MCP is a Model Context Protocol server that connects your Snowflake data warehouse to AI agents like Claude, ChatGPT, and Gemini. It lets you query tables, run analytics, manage warehouses, and monitor data pipelines in natural language — without writing SQL or navigating Snowflake's UI — all through Improvado's hosted MCP server.

Which Snowflake data can I access through the MCP server?

All databases, schemas, and tables in your Snowflake account that your service role has access to. This includes raw tables, views, materialized views, external stages, and Snowflake Marketplace data. The AI agent can query, join, and aggregate across any accessible object.

Can the AI agent create tables and run transformations, or only read data?

Both. Read operations include querying any accessible table with AI-generated SQL. Write operations include creating or modifying tables, running INSERT/UPDATE statements, managing warehouse sizes, and scheduling tasks. All write operations require a Snowflake role with appropriate privileges.

How does this handle large Snowflake warehouses with complex schema structures?

Improvado MCP uses Snowflake's INFORMATION_SCHEMA to give the AI agent structural awareness of your warehouse — table names, column types, relationships, and row counts. The agent can discover the right schema for a query without you specifying exact table names. For very large warehouses, you can scope the connection to specific databases or schemas.

Is my Snowflake data secure through the MCP server?

Yes. Improvado stores Snowflake credentials (key pair authentication or password) in an encrypted vault certified to SOC 2 Type II. The AI agent never accesses credentials directly. All queries are executed via Improvado's secure proxy using your designated Snowflake service role.

How quickly can I set this up?

Under 3 minutes. Provide your Snowflake account identifier, warehouse name, and authentication credentials, then configure the MCP server URL in your AI agent. Improvado customers with Snowflake already connected can start querying immediately.

Stop Reporting. Start Executing.

Connect your data to an AI agent in under 60 seconds. The closed loop starts with one conversation.

SOC 2 Type II GDPR 500+ Platforms