Bring Sql
to OpenAI Agents SDK
Create your Vinkius account to connect Google BigQuery to OpenAI Agents SDK and start using all 7 AI tools in minutes. Fully managed, enterprise secure, and ready to use without writing a single line of code. No hosting, no server setup — just connect and start using.
GDPR Free for SubscribersCompatible with every major AI agent and IDE
Gemini
What is the Google BigQuery MCP Server?
Connect your Google BigQuery data warehouse to any AI agent and empower it to act as a fractional data analyst. Traverse structured schemas, audit data pipelines, and execute complex aggregations over petabytes of data purely through conversational prompts.
What you can do
- Execute Queries — Prompt natively structural Data Analytics requests and allow the LLM to write, run, and summarize exact
Standard SQLinstantly - Discover Schemas — Inspect deep table column mappings, discovering strict clustering logic and native partitioning limits
- Audit Workloads — Paginate recent cluster jobs, identify heavily delayed computations globally, and read bytes explicitly processed by runs
- Dataset Topologies — Traverse nested datasets logically mapping GCP access properties recursively
- Performance Troubleshooting — Read exact job error traces directly confirming syntax failures natively
How it works
- Subscribe to this server
- Enter your GCP Project ID and an active OAuth/Service Account Token
- Start querying terabytes of rows securely from Claude, Cursor, or your preferred agent workspace
Stop switching into the GCP Console for quick data validations. Check database constraints and summarize recent daily logs all from your chat.
Who is this for?
- Data Engineers — troubleshoot failing scheduled queries and explore undocumented columns securely on-the-fly
- Marketing Analysts — request customer cohorts using conversational logic that natively translates to optimized SQL
- Backend Developers — rapidly confirm if application background pipelines successfully inserted the necessary rows without breaking flow
Built-in capabilities (7)
Run an explicit BigQuery Standard SQL command
Get exact details of a specific BigQuery dataset
Get complete details of a specific BigQuery Job run
Get explicit metadata and schema details of a pure BigQuery Table
List all explicit Datasets in the active GCP project
List recent explicit BigQuery runtime Jobs securely
List explicit Tables natively contained within a Dataset
Why OpenAI Agents SDK?
The OpenAI Agents SDK auto-discovers all 7 tools from Google BigQuery through native MCP integration. Build agents with built-in guardrails, tracing, and handoff patterns. chain multiple agents where one queries Google BigQuery, another analyzes results, and a third generates reports, all orchestrated through Vinkius.
- —
Native MCP integration via
MCPServerSse, pass the URL and the SDK auto-discovers all tools with full type safety - —
Built-in guardrails, tracing, and handoff patterns let you build production-grade agents without reinventing safety infrastructure
- —
Lightweight and composable: chain multiple agents and MCP servers in a single pipeline with minimal boilerplate
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First-party OpenAI support ensures optimal compatibility with GPT models for tool calling and structured output
Google BigQuery in OpenAI Agents SDK
Why run Google BigQuery with Vinkius?
The Google BigQuery connection runs on our fully managed, secure cloud infrastructure. We handle the hosting, maintenance, and security so you don't have to deal with servers or code. All 7 tools are ready to work instantly without any complex setup.
You stay in complete control of your data. Your AI only accesses the information you approve, keeping your sensitive passwords and private details completely safe. Plus, with automatic optimizations, your AI works faster and more efficiently.
* Every connection is hosted and maintained by Vinkius. We handle the security, updates, and infrastructure so you don't have to write code or manage servers. See our infrastructure
Over 4,000 integrations ready for AI agents
Explore a vast library of pre-built integrations, optimized and ready to deploy.
Connect securely in under 30 seconds
Generate tokens to authenticate and link external services in a single step.
Complete visibility into every agent action
Audit live requests, latency, success rates, and active security compliance policies.
Optimize spending and track token ROI
Analyze real-time token consumption and cost metrics detailed by connection.
Explore our live AI Agents Analytics dashboard to see it all working
This dashboard is included when you connect Google BigQuery using Vinkius. You will never be left in the dark about what your AI agents are doing with your tools.
Google BigQuery and 4,000+ other AI tools. No hosting, no code, ready to use.
Professionals who connect Google BigQuery to OpenAI Agents SDK through Vinkius don't need to write code, manage servers, or worry about security. Everything is pre-configured, secure, and runs automatically in the background.
Raw MCP | Vinkius | |
|---|---|---|
| Ready-to-use MCPs | Find and configure each manually | 4,000+ MCPs ready to use |
| Connection Setup | Manual coding & server setup | 1-click instant connection |
| Server Hosting | You host it yourself (needs 24/7 uptime) | 100% hosted & managed by Vinkius |
| Security & Privacy | Stored in plaintext config files | Bank-grade encrypted vault |
| Activity Visibility | Blind execution (no logs or tracking) | Live dashboard with real-time logs |
| Cost Control | Runaway AI token spend risk | Automatic budget limits |
| Revoking Access | Must delete files or code to stop | 1-click disconnect button |
How Vinkius secures
Google BigQuery for OpenAI Agents SDK
Every request between OpenAI Agents SDK and Google BigQuery is protected by our secure gateway. We automatically keep your sensitive data private, prevent unauthorized access, and let you disconnect instantly at any time.
Frequently asked questions
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.
How does the OpenAI Agents SDK connect to MCP?
Use MCPServerSse(url=...) to create a server connection. The SDK auto-discovers all tools and makes them available to your agent with full type information.
Can I use multiple MCP servers in one agent?
Yes. Pass a list of MCPServerSse instances to the agent constructor. The agent can use tools from all connected servers within a single run.
Does the SDK support streaming responses?
Yes. The SDK supports SSE and Streamable HTTP transports, both of which work natively with Vinkius.
MCPServerStreamableHttp not found
Ensure you have the latest version: pip install --upgrade openai-agents
Agent not calling tools
Make sure your prompt explicitly references the task the tools can help with.
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