BigQuery MCP Server

Aembit supports the official BigQuery MCP server, which lets AI agentsAI Agent: A software workload that authenticates to systems, requests credentials, and accesses resources, either on behalf of a person or on its own. Aembit secures AI agents with the same identity-first model it uses for any workload. User-driven agents such as Claude Desktop also carry a blended identity that ties access to both the user and the agent.Learn more explore datasets and run SQL queries through MCPModel Context Protocol: A standard protocol for AI agent and server interactions that defines how AI assistants communicate with external tools and data sources.Learn more(opens in new tab) tools.

This page describes how to configure BigQuery as an MCP serverMCP Server: A server that implements the Model Context Protocol to provide tools, resources, or data to AI agents and MCP clients.Learn more(opens in new tab) behind the Aembit MCP Identity GatewayMCP Identity Gateway: A component that brokers MCP traffic between MCP clients and target MCP servers, validating authorization and presenting Aembit-managed credentials on each request.Learn more. Each user authenticates with their own Google identity, and the Gateway injects their token into MCP requests.

Looking for direct BigQuery API access?

This page covers MCP tool access through Aembit’s MCP Identity Gateway. To broker credentials for the BigQuery REST API as a traditional Server WorkloadServer Workload: Server Workloads represent target services, APIs, databases, or applications that receive and respond to access requests from Client Workloads.Learn more, see the GCP BigQuery Server Workload guide instead.

Prerequisites

Before you begin, ensure you have the following:

• A Google Cloud project where you can enable APIs and create OAuth credentials
• A BigQuery dataset with data to query
• A configured Aembit MCP Identity Gateway

Requirements and considerations

Before you configure BigQuery, review these requirements and behaviors specific to BigQuery’s MCP server.

• Each user needs their own BigQuery access. The Gateway authenticates with each user’s Google identity, so BigQuery enforces that user’s IAM roles rather than a shared service account. Also grant roles/bigquery.jobUser; without it, BigQuery rejects the execute_sql tool.
• This guide grants read-only access. It uses the bigquery.readonly scope, which covers browsing datasets and running queries. Add a broader scope if your agents need to modify data.

Set up your Google Cloud Platform project

Configure a Google Cloud project with the BigQuery API, user permissions, and an OAuth client.

1. In the Google Cloud console, select the project you want to use.

2. Enable the BigQuery API (bigquery.googleapis.com) for the project.

3. Grant each user who queries BigQuery the roles/bigquery.dataViewer and roles/bigquery.jobUser IAM roles.

4. Go to the Credentials page, click Create Credentials, then select OAuth client ID. If your project has no consent screen, configure one first: choose a User type, enter the app details, then add the https://www.googleapis.com/auth/bigquery.readonly scope.

5. For Application type, select Web application, enter a name, then click Create. Leave Authorized redirect URIs empty for now. You add the Aembit Callback URL after you create the Credential Provider.

6. Copy the Client ID and Client secret, and store them for the next section.

Configure the Credential Provider

Create an MCP User-Based Access Token Credential ProviderCredential Provider: Credential Providers obtain the specific access credentials—such as API keys, OAuth tokens, or temporary cloud credentials—that Client Workloads need to authenticate to Server Workloads.Learn more in Aembit.

1. Log into your Aembit Tenant.

2. Go to Credential Providers in the left sidebar and click + New.

3. Configure the following fields:

   Field	Value
   Name	A user-friendly name
   Credential Type	MCP User-Based Access Token
   MCP Server URL	https://bigquery.googleapis.com/mcp
   Client ID	The Client ID you copied from Google Cloud
   Client Secret	The Client Secret you copied from Google Cloud
   Scopes	https://www.googleapis.com/auth/bigquery.readonly
   PKCE Required	On

   For MCP Server URL, click Discover to populate the Authorization URL and Token URL.

4. Click Save.

5. Copy the read-only Callback URL from the Credential Provider.

Finish configuring the OAuth client

Return to the OAuth client in the Google Cloud console.

1. Open the Web application OAuth client you created.

2. Under Authorized redirect URIs, click Add URI, paste the Aembit Callback URL, then click Save.

Authorize the Credential Provider

1. Return to the Credential Provider in Aembit and click Authorize.

2. Choose your Google Account and approve access. The Credential Provider status changes to Ready when the flow completes.

Create the Server Workload

1. Go to Server Workloads in the left sidebar and click + New.

2. Configure the following fields:

   Field	Value
   Name	A user-friendly name
   Host	bigquery.googleapis.com
   Application Protocol	MCP
   Port	443 with TLS
   URL Path	/mcp
   Authentication method	HTTP Authentication
   Authentication scheme	Bearer

3. Click Save.

Create an Access Policy

Create an Access PolicyAccess Policy: Access Policies define, enforce, and audit access between Client and Server Workloads by cryptographically verifying workload identity and contextual factors rather than relying on static secrets.Learn more linking your Client WorkloadClient Workload: Client Workloads represent software applications, scripts, or automated processes that initiate access requests to Server Workloads, operating autonomously without direct user interaction.Learn more (the AI agent), the MCP User-Based Access Token Credential Provider, and the BigQuery Server Workload. See Access Policies for details.

Verify

After a user authorizes access, the Aembit AI Access Authorized page lists the BigQuery MCP Server as Ready. The AI agent can then call BigQuery MCP tools (such as execute_sql) through the Gateway.