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One MCP connection. Full Snowflake context. No more tab-switching — just ask.
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.
Your AI agent reads harmonized data across 500+ platforms. "Cost" in Google Ads and "spend" in Meta Ads resolve to the same field automatically.
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.
250+ governance rules enforce naming conventions, budget limits, and KPI thresholds. SOC 2 Type II certified.
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.
Automated weekly reports, anomaly flagging, and budget alerts — all from a single conversation. No more morning check-ins across 5 dashboards.
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.
Every phase runs through the same MCP connection. One protocol, all platforms, full governance. No switching between tools.
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.
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.
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.
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.
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.
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.
Same MCP connection, different workflows for every team member. Agency CEOs get portfolio health. Media Strategists get campaign QA. Analysts get cross-platform reports. Account Managers get auto-generated QBR decks. Creative Directors get performance-based briefs.
Each role asks in natural language. The MCP server handles the complexity — rate limits, auth, schema normalization, governance — behind the scenes.
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 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.
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.
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.
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.
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.
Connect your data to an AI agent in under 60 seconds. The closed loop starts with one conversation.
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