BigQuery MCP Server Generator for AI Agents
Give Claude, ChatGPT, Cursor and your own agents governed access to BigQuery tables through named tools instead of a service account with free SQL. APIGenerator generates an MCP server for the datasets you choose, with a filter per key, column limits, a cap on bytes scanned and a log of every call.
Your generated API
https://clinic.apigenerator.ai
- Parsing source
- Inferring resources
- Designing endpoints
- Writing docs
- Building MCP tools
Request
Response
Create your account to see every resource, run the tests, export the code and deploy this API.
Why free SQL on a warehouse is the wrong way to connect agents
BigQuery bills by data scanned. A generic warehouse MCP server lets the model write any query, so one careless prompt can scan a multi terabyte events table and land on next month's invoice. The same freedom is a privacy problem. A service account that can read the dataset can also read the columns with emails, revenue per customer or internal notes, and a prompt injected through a support ticket can ask for them. Named tools with fixed shapes, one key per agent and a byte cap per call remove both risks.
What the generated BigQuery MCP server does
- Turns the tables and views you pick into tools with typed filters, date ranges and a hard limit on rows returned.
- Exposes only approved columns. Sensitive columns stay out of every tool description and every result.
- Applies a filter per key, such as account_id or region, to every query, so one agent sees one customer's rows.
- Rejects calls that would scan more than the limit you set, and requires a partition filter on large tables.
- Keeps the service account on the server. The model supplies values, never SQL text.
- Writes a log line for every call with the key, the tool, the arguments, the rows returned and the bytes scanned.
From datasets to a working agent in four steps
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Describe the tables and questions the agent must answer, or upload a schema export or an OpenAPI file.
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Review the proposed tools and change names, descriptions, columns and limits.
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Choose output. MCP server is preselected, and the same definition also gives you a REST API and GraphQL.
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Deploy to managed hosting or export the code, then add the server URL and a key to your agent.
Teams that put agents in front of warehouse data
- Data and analytics teams that want an analyst agent on sales, product or marketing tables without a surprise bill.
- SaaS companies whose customers ask questions about their own usage data stored in a shared warehouse.
- RevOps and finance teams who need an agent to read approved reporting views and nothing else.
- Agencies and consultancies that build agents for several clients and need a separate key and filter set for each.
Generic warehouse MCP server or a generated one
A generic server is a quick way to explore a sandbox project on your laptop. The table describes what changes when the agent reaches production tables and finance asks who is running queries.
| Topic | Generic BigQuery MCP server | Generated with APIGenerator |
|---|---|---|
| Access model | One service account, free SQL | Named tools, one key per agent |
| Query written by | The model | Fixed tools with typed arguments |
| Cost control | Whatever the account allows | Cap on bytes scanned per call |
| Tenant isolation | Your own discipline in every prompt | Per key filter applied on the server |
| Column exposure | Every column the account can read | Only approved columns |
| Audit trail | Your own logging | Built in per call, with bytes scanned |
| Same definition as REST or GraphQL | No | Yes |
Other data sources and outputs
Agents on an operational database? See the postgres mcp server, mysql mcp server and mongodb mcp server pages. For CRM data see the salesforce mcp server and hubspot mcp server pages. To put many agents behind one governed endpoint use the mcp gateway, and for plans see pricing.
Questions about bigquery mcp server
Yes. Each tool has a limit on bytes scanned and rows returned, and large tables can require a partition filter. A call that would exceed the limit is rejected before it runs, so the agent cannot create a surprise bill.
No. The generated server keeps the credentials on your side and agents call named tools with their own key. The model never sees the credentials and never writes SQL.
Yes. A filter such as account_id is attached to the key, and every call by that agent is restricted to matching rows. Hidden columns never appear in results.
Yes. Views are a good fit because they already hold approved columns and business logic. Pick them as the source for tools and the agent reaches nothing else.
Connect agents to BigQuery with rules you control
Describe your datasets and see the generated tools before you pay for anything.