google-bigquery · MCP Server

Google BigQuery + Improvado MCP — Warehouse Queries Made Simple

Improvado gives your AI agent direct access to Google BigQuery through an MCP server. Query tables, analyze trends, and explore datasets in natural language — no SQL required. Works with Claude, ChatGPT, Cursor, and any MCP-compatible tool.

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

Read

Read: Query BigQuery Without Writing SQL

Your AI agent becomes a direct interface to your data warehouse. Ask questions in plain English and get answers from BigQuery tables across any project, dataset, and date range. The MCP server handles query generation and execution.

Example prompts

"What's the revenue trend by product category for Q1 vs Q4? Pull from the sales_transactions dataset."

45 min → 1 min

"Show me the top 20 customers by lifetime value. Include their acquisition channel and first purchase date."

30 min → 30 sec

"Compare CAC by marketing channel over the last 12 months. Break it down by region and product tier."

4 hrs → 3 min

Works with Claude ChatGPT Cursor +5

Write

Write: Create and Update BigQuery Objects Through Chat

Your AI agent doesn't just query BigQuery — it manages it. Create views, schedule queries, update table schemas, and run transformation jobs through natural language without opening the BigQuery console.

Example prompts

"Create a view in the analytics dataset that shows daily active users segmented by acquisition channel for the last 90 days."

2 hrs → 10 min

"Schedule a daily query that refreshes the revenue_summary table every morning at 6am UTC."

1 hr → 5 min

"Add a new column 'churn_risk_score' to the customer_features table and backfill it using the existing model output."

3 hrs → 15 min

Every action logged · Fully reversible · SOC 2 certified

Monitor

Monitor: Watch Data Quality and Pipeline Health

Set up watches on data freshness, row counts, and query costs. Your AI agent monitors BigQuery pipelines continuously and flags anomalies before they affect downstream reports.

Example prompts

"Alert me if the daily_events table hasn't been updated in more than 26 hours."

Manual → auto

"Every morning: check that all pipeline tables were refreshed and report any that missed their scheduled update window."

1 hr → auto

"Flag any query job that consumed more than 10TB of data in the last 24 hours."

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

Non-Technical Teams Blocked by SQL Requirements

The problem

Marketing, finance, and operations teams need BigQuery data but can't write SQL. Every data request goes to the data engineering queue, adding days to analyses that should take minutes. Self-serve BI tools cover basic dashboards but can't handle complex ad-hoc questions.

How MCP solves it

Improvado's MCP server translates natural language into BigQuery SQL, executes it, and returns formatted results. Non-technical teams query terabytes of warehouse data in plain English — no SQL training, no ticket backlog.

Try asking

What's the average time between signup and first purchase for customers acquired through paid search last quarter?

Answer in seconds

All data sources, one query

Challenge 2

Cross-Project Queries Require Complex IAM Setup

The problem

Data warehouses span multiple GCP projects — analytics, production, staging, marketing. Running queries across projects requires coordinating service account permissions and writing cross-project SQL that few team members understand.

How MCP solves it

Improvado manages BigQuery service account credentials and cross-project access centrally. AI agents can query across all authorized projects in a single question without understanding GCP IAM or cross-project SQL syntax.

Try asking

Compare revenue data from the production project with attribution data from the marketing project for Q1.

Full detail preserved

No data loss on export

Challenge 3

Runaway Query Costs Discovered After the Fact

The problem

Teams writing ad-hoc BigQuery queries often scan far more data than needed, generating unexpected costs. Billing alerts trigger after the spend happens, and tracking which queries caused the overrun requires manual log analysis.

How MCP solves it

Improvado's MCP server estimates query costs before execution and applies configurable data scan limits. AI agents surface cost estimates with results, and monitoring watches flag expensive query patterns in real time.

Try asking

What were our top 5 most expensive BigQuery queries yesterday? Who ran them and what were they scanning?

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

Does Google BigQuery have an MCP server?

Google has released experimental MCP tooling for BigQuery as part of its broader AI agent ecosystem. Improvado offers a hosted alternative — no local installation, pre-connected GCP projects, and BigQuery data available alongside 1,000+ other marketing and analytics data sources through the same MCP connection.

What BigQuery operations can I perform through the MCP server?

Read operations: querying tables and views, schema exploration, dataset browsing, query history, and cost analysis. Write operations: creating and updating views, scheduling queries, modifying table schemas, and triggering data transfers. Improvado translates natural language into BigQuery SQL automatically.

Which AI tools work with BigQuery through this MCP server?

Any tool supporting the Model Context Protocol — Claude Desktop, ChatGPT, Cursor, Windsurf, Gemini, and custom applications using MCP HTTP transport. Claude is most commonly used due to its native MCP support and strong SQL generation capabilities — all through Improvado's hosted MCP server.

How does Improvado control BigQuery query costs?

Improvado applies configurable data scan limits per query, estimates costs before execution, and surfaces cost information with query results. Teams can set project-level or user-level spend limits that prevent runaway scans while still enabling flexible ad-hoc analysis.

Can I query BigQuery alongside other data sources through the same MCP?

Yes. Improvado connects 1,000+ data sources through the same MCP server. Teams can combine BigQuery warehouse data with real-time marketing platform data from Google Ads, Salesforce, HubSpot, and others — enabling unified analysis without data movement.

Is BigQuery data secure through the MCP server?

Yes. Improvado is SOC 2 Type II certified. GCP service account credentials are stored in an encrypted vault and never exposed to AI agents. All queries execute through Improvado's secure proxy with full audit logging. Column-level access controls can be configured to restrict sensitive data.

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