Google BigQuery Connector for AI agents.
7 live capabilities
Query your data warehouse with natural language.
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Why people use Google BigQuery
Stop Hunting Through the Google BigQuery Console for Data
With this Connector, you just ask your agent for the answer. It finds the right tables, writes the SQL, and gives you the summary in your chat window. You get your data without the tab-switching headache.
What Vinkius changes
You get a conversational interface for your entire GCP data warehouse without leaving your workspace.
Use it from Claude, ChatGPT, Cursor or another AI client you already have.
One account · 5,900+ Connectors
- Real-world use case 01
Marketing signup analysis
The agent uses `execute_query` to run the SQL and summarizes the results in a clear list of top countries.
- Real-world use case 02
Ops pipeline monitoring
The agent uses `list_jobs` to find the specific ID and reports the status and error trace for a failed sync.
- Real-world use case 03
Schema exploration
The agent uses `get_table` to list the schema so you can confirm the data structure of a production table.
Complete set · 7capabilities
The complete Google BigQuery capability set.
These are the exact actions your AI can choose when you ask it to work with Google BigQuery.
01—04
4 capabilities in this set.
Part of 7 available through Google BigQuery.
- 01 Capability
List datasets
See all the datasets available in your active GCP project. This helps your agent navigate your data warehouse structure.
- 02 Capability
Get dataset
Get the exact details of a specific dataset. This provides context on permissions and properties for a group of tables.
- 03 Capability
List tables
Get a list of all tables inside a specific dataset. This lets your agent find the right data source for a specific request.
- 04 Capability
Get table
Fetch the specific metadata and column details for a BigQuery table. This helps your agent understand the data structure before it runs a query.
05—07
3 capabilities in this set.
Part of 7 available through Google BigQuery.
- 05 Capability
Execute query
Run a Standard SQL command against your dataset. This is the primary way your agent interacts with your data to get answers.
- 06 Capability
List jobs
See a list of recent runtime jobs in your project. This lets you track progress and identify stalled computations.
- 07 Capability
Get job
Pull the full details of a single job run. Use this to see specific error messages or bytes processed for a single task.
Set up in minutes
One URL. Then ask Google BigQuery to work.
Claude and ChatGPT only need the Connector URL. Copy it once, add it in settings, and use Google BigQuery from the conversation.
Choose your client
Live previewAdvanced clients IDE · CLI
Claude · Web + desktop
Connector URL · ready to paste
Streamable HTTPhttps://edge.vinkius.com/vk_preview_XL1CHYzMkCvLxlGLPBzLHDOTg35sqGtg0BPtLGTY/mcp - Step 01
Open Connectors
In Claude Web or Claude Desktop, open Settings and choose Connectors.
- Step 02
Add the URL
Choose Add custom connector, name it Google BigQuery, and paste the URL above.
- Step 03
Turn it on in chat
Select +, open Connectors, and enable Google BigQuery for the conversation.
ChatGPT · Web + desktop
Connector URL · ready to paste
Streamable HTTPhttps://edge.vinkius.com/vk_preview_XL1CHYzMkCvLxlGLPBzLHDOTg35sqGtg0BPtLGTY/mcp - Step 01
Open MCP settings
On desktop, open Settings and MCP servers. On web, open your workspace app or connector settings.
- Step 02
Add the URL
Choose Add server with Streamable HTTP, or create a custom MCP app, then paste the Google BigQuery URL.
- Step 03
Save and start
Save the connection and enable Google BigQuery in your conversation. Desktop may ask you to restart once.
Cursor · IDE configuration
Advanced setup
{
"mcpServers": {
"google-bigquery": {
"url": "https://edge.vinkius.com/vk_preview_XL1CHYzMkCvLxlGLPBzLHDOTg35sqGtg0BPtLGTY/mcp"
}
}
}- Step 01
Open MCP Settings
Press Cmd+Shift+P (macOS) or Ctrl+Shift+P (Windows/Linux) → search "MCP Settings"
- Step 02
Add the server config
Paste the JSON configuration above into the mcp.json file that opens
- Step 03
Save the file
Cursor will automatically detect the new Connector
- Step 04
Start using Google BigQuery
Open Agent mode in chat and ask: "Using Google BigQuery, help me...". 7 tools available
VS Code Copilot · IDE configuration
Advanced setup
{
"mcpServers": {
"google-bigquery": {
"url": "https://edge.vinkius.com/vk_preview_XL1CHYzMkCvLxlGLPBzLHDOTg35sqGtg0BPtLGTY/mcp"
}
}
}- Step 01
Create MCP config
Create a .vscode/mcp.json file in your project root
- Step 02
Add the server config
Paste the JSON configuration above
- Step 03
Enable Agent mode
Open GitHub Copilot Chat and switch to Agent mode using the dropdown
- Step 04
Start using Google BigQuery
Ask Copilot: "Using Google BigQuery, help me...". 7 tools available
Windsurf · IDE configuration
Advanced setup
{
"mcpServers": {
"google-bigquery": {
"url": "https://edge.vinkius.com/vk_preview_XL1CHYzMkCvLxlGLPBzLHDOTg35sqGtg0BPtLGTY/mcp"
}
}
}- Step 01
Open MCP Settings
Go to Settings → MCP Configuration or press Cmd+Shift+P and search "MCP"
- Step 02
Add the server
Paste the JSON configuration above into mcp_config.json
- Step 03
Save and reload
Windsurf will detect the new server automatically
- Step 04
Start using Google BigQuery
Open Cascade and ask: "Using Google BigQuery, help me...". 7 tools available
Cline · IDE configuration
Advanced setup
{
"mcpServers": {
"google-bigquery": {
"url": "https://edge.vinkius.com/vk_preview_XL1CHYzMkCvLxlGLPBzLHDOTg35sqGtg0BPtLGTY/mcp"
}
}
}- Step 01
Open Cline MCP Settings
Click the Connectors icon in the Cline sidebar panel
- Step 02
Add remote server
Click "Add Connector" and paste the configuration above
- Step 03
Enable the server
Toggle the server switch to ON
- Step 04
Start using Google BigQuery
Ask Cline: "Using Google BigQuery, help me...". 7 tools available
Claude Code · Terminal command
Advanced setup
claude mcp add google-bigquery --transport http "https://edge.vinkius.com/vk_preview_XL1CHYzMkCvLxlGLPBzLHDOTg35sqGtg0BPtLGTY/mcp"- Step 01
Install Claude Code
Run npm install -g @anthropic-ai/claude-code if not already installed
- Step 02
Add the Connector
Run the command above in your terminal
- Step 03
Verify the connection
Run claude mcp to list connected servers, or type /mcp inside a session
- Step 04
Start using Google BigQuery
Ask Claude: "Using Google BigQuery, show me...". 7 tools are ready
Where the request belongs
Work Google BigQuery can move forward.
The data engineer tired of manual SQL, the marketing analyst who needs quick stats, and the backend dev checking pipeline health.
Data Engineer
Troubleshoots failing scheduled queries and explores undocumented columns during maintenance.
Marketing Analyst
Requests customer cohorts and signups using conversational logic that turns into SQL.
Backend Developer
Confirms that background pipelines successfully inserted rows without breaking the flow.
Build the capability set
Add more capabilities.
Each Connector adds new actions and data without changing how you work.
Browse Connectors
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Snowflake
Execute SQL queries, manage databases, and analyze data on Snowflake with AI agents.
ClickHouse
Query and manage ClickHouse databases. execute analytical SQL, monitor replication status, and manage schemas directly from your AI agent.
StarRocks
High-performance analytical database. manage clusters, tables, and query data via AI.
data.world
Equip your AI agent to discover and manage data assets, projects, and queries directly via the data.world API.
QuestDB (Time-Series)
High-performance time-series database for fast SQL queries, data ingestion, and real-time analytics directly from your AI agent.
Bring your own AI
Change the model, client or framework. Keep Google BigQuery connected.
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Claude -
ChatGPT -
Gemini -
Cursor -
VS Code -
Windsurf -
ZCode -
Cline -
Zed -
Continue -
Kiro -
Roo Code -
Zencoder -
Goose -
Void -
Augment Code -
Amp -
Qodo -
Tabnine -
Pieces -
Sourcegraph Cody -
JetBrains -
Warp -
Amazon Q -
Antigravity -
BoltAI -
Raycast -
Jan -
LM Studio -
AnythingLLM -
Open WebUI -
Msty -
Cherry Studio -
LibreChat -
TypingMind -
Chorus -
5ire -
n8n -
LangChain -
LlamaIndex -
CrewAI -
Vercel AI SDK
Before you connect
Questions about Google BigQuery.
The practical details behind the request, access and result.
Can I use the Google BigQuery MCP with Claude or Cursor?
Yes, this Connector works with any AI client that supports the Model Context Protocol, including Claude, Cursor, and Windsurf.
Is my data secure when using the Google BigQuery MCP?
Your data remains secure. The Connector uses your own GCP credentials and permissions to access your data warehouse.
Can the Google BigQuery MCP handle very large datasets?
Yes, it interacts directly with your BigQuery warehouse, meaning it can query and summarize data across petabytes of information.
Do I need to know SQL to use the Google BigQuery MCP?
No, you don't need to write SQL. You can ask your agent questions in plain English, and it will generate and run the SQL for you.
How does the Google BigQuery MCP help with debugging?
It can pull exact error traces from failed jobs, showing you the specific syntax error or permission issue that stopped your pipeline.
Can I use the Google BigQuery MCP to see my table schemas?
Yes, your agent can inspect the metadata of any table to understand the columns, types, and partitioning logic before running a query.
Can my AI write its own queries if I just ask it a business question?
Yes! The agent will typically use list_tables and get_table to study the columns first. Then, realizing constraints, it will natively invoke execute_query running an optimized Standard SQL string to fetch exactly what you asked for.
Will my prompt fail if it returns millions of rows?
It might hit the context window boundaries of the chosen foundational LLM. Good practice suggests instructing your AI to always append LIMIT 100 initially or run macro aggregations (like COUNT() or SUM()) natively inside BigQuery first.
How do I check if a query was expensive after it ran?
Use the list_jobs or get_job endpoints. They expose metadata directly from Google's history returning the totalBytesProcessed flag so your agent can estimate overhead intelligently.
One connection away
Give your agent a direct line to Google BigQuery.
Connect Google BigQuery once. Keep it beside 5,900+ managed Connectors when the next task needs more.
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