BigQuery MCP Server

What is this? 🤔

This is a server that lets your LLMs (like Claude) talk directly to your BigQuery data! Think of it as a friendly translator that sits between your AI assistant and your database, making sure they can chat securely and efficiently.

Quick Example

You: "What were our top 10 customers last month?"
Claude: *queries your BigQuery database and gives you the answer in plain English*

No more writing SQL queries by hand - just chat naturally with your data!

How Does It Work? 🛠️

This server uses the Model Context Protocol (MCP), which is like a universal translator for AI-database communication. While MCP is designed to work with any AI model, right now it's available as a developer preview in Claude Desktop.

Here's all you need to do:

1. Set up authentication (see below)
2. Add your project details to Claude Desktop's config file
3. Start chatting with your BigQuery data naturally!

What Can It Do? 📊

• Run SQL queries by just asking questions in plain English
• Access both tables and materialized views in your datasets
• Explore dataset schemas with clear labeling of resource types (tables vs views)
• Analyze data within configurable safe limits (configured via config.json)
• Protect sensitive data — define field-level access restrictions to prevent AI agents from reading PII, PHI, financial data, and secrets. The agent receives clear guidance on how to reformulate queries using aggregates or EXCEPT clauses, so it remains useful without exposing individual records.
• Auto-discover sensitive fields — automatically scan your entire BigQuery data warehouse for columns matching sensitive patterns (names, emails, SSNs, medical records, API keys, etc.) and add them to the restricted list. New tables and columns are protected automatically on each scan — no manual maintenance required.
• Fully configurable — everything is driven by config.json. Add your own detection patterns to match your organization's naming conventions (e.g., %guardian_name%, %beneficiary%), adjust scan frequency, set billing limits, and define per-table field restrictions. The scanner picks up your custom patterns on the next run and automatically protects any matching columns across all datasets.

Which Setup Is Right for You?

Why local deployment matters for sensitive data: LLM inference happens in the cloud. When an AI agent queries BigQuery, the results are sent to the LLM provider's servers (Anthropic, OpenAI, etc.) for processing — they leave your network. BigQuery IAM controls who can reach your data; field restrictions control what the AI agent surfaces into LLM responses. These are different protection boundaries. Configuring preventedFields ensures PHI and PII never enter the LLM conversation context, regardless of how many queries the agent runs autonomously.

Quick Start 🚀

Prerequisites

• Node.js 14 or higher
• Google Cloud project with BigQuery enabled
• Either Google Cloud CLI installed or a service account key file
• Claude Desktop (currently the only supported LLM interface)

Option 1: Quick Install via Smithery (Recommended — Simple Mode)

To install BigQuery MCP Server for Claude Desktop automatically via Smithery, run this command in your terminal:

npx @smithery/cli install @ergut/mcp-bigquery-server --client claude

The installer will prompt you for:

• Your Google Cloud project ID
• BigQuery location (defaults to us-central1)

Once configured, Smithery will automatically update your Claude Desktop configuration and restart the application.

Option 2: Manual Setup (Protected Mode — for sensitive data)

If you handle sensitive data or need field-level access restrictions:

Note: The npx @ergut/mcp-bigquery-server command installs the published upstream package, which does not yet include Protected Mode. Until this PR is merged and a new version is published, use the local build below.

1. Authenticate with Google Cloud (choose one method):

   • Using Google Cloud CLI (great for development):

     gcloud auth application-default login
     

   • Using a service account (recommended for production):

     # Save your service account key file and use --key-file parameter
     # Remember to keep your service account key file secure and never commit it to version control
     

2. Clone this fork and build locally:

   git clone https://github.com/drharunyuksel/mcp-bigquery-server
   cd mcp-bigquery-server
   npm install && npm run build
   

3. Add to your Claude Desktop config Add this to your claude_desktop_config.json:

   • Basic configuration with config file:

     {
       "mcpServers": {
         "bigquery": {
           "command": "node",
           "args": [
             "/path/to/mcp-bigquery-server/dist/index.js",
             "--project-id",
             "your-project-id",
             "--location",
             "us-central1",
             "--config-file",
             "/path/to/config.json"
           ]
         }
       }
     }
     

   • With service account and config file:

     {
       "mcpServers": {
         "bigquery": {
           "command": "node",
           "args": [
             "/path/to/mcp-bigquery-server/dist/index.js",
             "--project-id",
             "your-project-id",
             "--location",
             "us-central1",
             "--key-file",
             "/path/to/service-account-key.json",
             "--config-file",
             "/path/to/config.json"
           ]
         }
       }
     }
     

4. Start chatting! Open Claude Desktop and start asking questions about your data.

Configuration

The server supports an optional config.json file for advanced configuration. Without a config file (i.e., no --config-file flag), the server runs in Simple Mode with safe defaults (1GB query limit, no field restrictions). To enable protection, pass --config-file /path/to/config.json when starting the server.

config.json Structure

{
  "maximumBytesBilled": "1000000000",
  "preventedFields": {
    "healthcare.patients": ["first_name", "last_name", "ssn", "date_of_birth", "email"],
    "billing.transactions": ["credit_card_number", "bank_account"]
  },
  "sensitiveFieldPatterns": [
    "%first_name%", "%last_name%", "%email%",
    "%ssn%", "%date_of_birth%", "%password%"
  ],
  "sensitiveFieldScanFrequencyDays": 1
}

Command Line Arguments

• --project-id: (Required) Your Google Cloud project ID
• --location: (Optional) BigQuery location, defaults to 'US'
• --key-file: (Optional) Path to service account key JSON file
• --config-file: (Optional) Path to configuration file, defaults to 'config.json'
• --maximum-bytes-billed: (Optional) Override maximum bytes billed for queries, overrides config.json value

Example using service account:

npx @ergut/mcp-bigquery-server --project-id your-project-id --location europe-west1 --key-file /path/to/key.json --config-file /path/to/config.json --maximum-bytes-billed 2000000000

Protecting Sensitive Data 🔒

Data warehouses often contain highly sensitive information — patient records, social security numbers, financial data, personal contact details, and authentication secrets. When an AI agent has direct access to query your BigQuery warehouse, there is no human in the loop to prevent it from reading sensitive columns. A simple query like SELECT * FROM patients could expose thousands of PII/PHI records in a single response.

This server gives administrators fine-grained control over which columns an AI agent can access, ensuring sensitive data stays protected while still allowing the AI to perform useful analytical queries on non-sensitive fields.

Security Model: Cooperative Guardrails, Not a SQL Firewall

Important: The field restrictions and table allowlists in this server are designed as cooperative guardrails for AI agents, not as a hard security boundary against adversarial attackers.

The threat model is straightforward: when an AI agent queries your BigQuery warehouse, the query results are sent to the LLM provider's servers. Field restrictions prevent the agent from inadvertently including sensitive columns (PII, PHI, secrets) in those results. When the agent encounters a restriction error, it reads the guidance in the error message and reformulates its query — using aggregate functions, EXCEPT clauses, or simply dropping the restricted field. In practice, AI agents cooperate immediately and consistently.

This system uses regex-based SQL analysis to detect restricted field usage. We performed penetration testing during development and fixed several bypass vectors (struct-alias expansion, comma-join evasion, implicit SELECT *). However, regex-based parsing cannot guarantee coverage of every possible SQL construct — edge cases involving deeply nested CTEs, exotic BigQuery syntax, or adversarial query crafting may exist. The enforcement logic is designed to fail closed (block ambiguous queries rather than allow them), but it is not equivalent to a database-level security policy.

What this is:

• Effective guidance that prevents AI agents from accessing sensitive data in normal usage
• A safety net that catches common query patterns (SELECT *, direct field references, aliases)
• Hardened against known bypass techniques discovered through penetration testing

What this is not:

• A replacement for BigQuery IAM, column-level security, or row-level access policies
• A defense against a malicious human deliberately crafting bypass queries
• A certified SQL parser — it uses pattern matching, not a full AST

For environments requiring strict compliance guarantees, combine these guardrails with BigQuery's native column-level security and authorized views.

Protection Modes

The server supports three protection modes, configured via protectionMode in config.json:

allowedTables Mode

Restricts the AI agent to a specific set of tables. Queries against any other table are rejected immediately. Optionally define field restrictions within allowed tables:

{
  "protectionMode": "allowedTables",
  "maximumBytesBilled": "10000000000",
  "allowedTables": [
    "analytics.page_views",
    "analytics.sessions",
    "reporting.daily_summary"
  ],
  "preventedFieldsInAllowedTables": {
    "analytics.page_views": ["user_ip", "user_agent"]
  }
}

• preventedFieldsInAllowedTables is optional — defaults to {} (no field restrictions within allowed tables)
• The auto-scan does not run in this mode — all restrictions are manually configured
• INFORMATION_SCHEMA queries are always allowed for schema discovery

autoProtect Mode (Field-Level Restrictions)

The original protection mode. Auto-scans your BigQuery datasets for sensitive columns and enforces preventedFields. Manual entries in preventedFields persist through scans (the merge is additive-only). See details below.

Existing config files without protectionMode continue working — they default to autoProtect for backward compatibility.

Field-Level Access Restrictions

Define preventedFields in your config to block the AI agent from accessing specific columns:

{
  "preventedFields": {
    "healthcare.patients": ["first_name", "last_name", "ssn", "date_of_birth", "email"],
    "billing.transactions": ["credit_card_number", "bank_account"]
  }
}

What happens when the AI agent tries to access a restricted field:

SELECT first_name, last_name, diagnosis FROM healthcare.patients

The server blocks the query and returns a clear, instructive error:

Restricted fields detected for table "healthcare.patients" columns "first_name", "last_name".
You can only use these columns inside ["count", "countif", "avg", "sum"]
aggregate functions or exclude them with SELECT * EXCEPT (...).

The AI agent learns from this error and adjusts its queries automatically. It can still run analytical queries that don't expose individual sensitive values:

-- Allowed: aggregate functions don't expose individual values
SELECT COUNT(first_name) AS patient_count, diagnosis
FROM healthcare.patients
GROUP BY diagnosis

-- Allowed: explicitly excluding restricted fields
SELECT * EXCEPT(first_name, last_name, ssn, date_of_birth, email)
FROM healthcare.patients

Query pattern reference:

Note: Restricted fields in WHERE, ORDER BY, and other clauses are blocked, not just fields in SELECT. Even though the query results don't contain the restricted column, the full SQL query text is sent to the LLM provider as part of the conversation. A query like WHERE email = '[email protected]' means the restricted value appears in the prompt sent to the cloud. The enforcement checks the entire query to prevent restricted data from leaving your network in any form.

Server-side logging: Every blocked query is logged on the server side, giving administrators visibility into what the AI agent attempted to access:

Query tool error: Error: Restricted fields detected for table "healthcare.patients" columns "first_name", "last_name".

Automated Sensitive Field Scanner

Manually listing every sensitive column across hundreds of tables is impractical. The server includes an automated scanner that discovers sensitive columns across all your BigQuery datasets by querying INFORMATION_SCHEMA.COLUMNS with configurable SQL LIKE patterns. Discovered fields are automatically added to preventedFields in your config.

How It Works

1. The scanner runs SQL LIKE pattern matching against all column names in your BigQuery project
2. Columns matching patterns like %first_name%, %ssn%, %email% are identified as sensitive
3. Discovered columns are merged into your config's preventedFields
4. The merge is additive-only — manually added restrictions are never removed

Auto-Scan on Server Startup

When the MCP server starts, it checks if the config file is stale based on sensitiveFieldScanFrequencyDays. If stale, it automatically scans and updates the config:

Config is stale (scan frequency: 1 day(s)), running sensitive field scan...
Scanning all datasets for sensitive fields...
Found 1166 sensitive column(s) across 278 table(s)
Scan complete: config updated with 278 tables.

This means new tables with sensitive columns are automatically protected without any manual configuration. As your data warehouse grows, the scanner keeps up.

Manual Scan via CLI

Run a scan on demand at any time:

npm run scan-fields -- --project-id your-project-id --config-file ./config.json

Custom Patterns for Your Organization

The default patterns cover common naming conventions (names, emails, SSNs, dates of birth, medical record numbers, insurance IDs, passwords, API keys, etc.), but every organization has its own. Add custom patterns to match your schema:

{
  "sensitiveFieldPatterns": [
    "%first_name%", "%last_name%", "%email%", "%ssn%",
    "%date_of_birth%", "%password%", "%api_key%",
    "%guardian_name%",
    "%emergency_contact%",
    "%beneficiary%",
    "%next_of_kin%"
  ]
}

On the next auto-scan (or manual npm run scan-fields), the scanner picks up columns matching your new patterns and automatically adds them to preventedFields. As your data warehouse grows and new tables are added, any columns matching your patterns are automatically protected without manual intervention.

Scanner Configuration

Permissions Needed

You'll need one of these:

• roles/bigquery.user (recommended)
• OR both:
  • roles/bigquery.dataViewer
  • roles/bigquery.jobUser

Local Build (Optional) 🔧

Run a local build instead of npx — useful for contributing, testing changes, or running a pinned version. Supports both Simple and Protected Mode.

# Clone and install
git clone https://github.com/ergut/mcp-bigquery-server
cd mcp-bigquery-server
npm install

# Build
npm run build

Then update your Claude Desktop config to point to your local build:

• Simple Mode (no config file):

  {
    "mcpServers": {
      "bigquery": {
        "command": "node",
        "args": [
          "/path/to/your/clone/mcp-bigquery-server/dist/index.js",
          "--project-id",
          "your-project-id",
          "--location",
          "us-central1"
        ]
      }
    }
  }
  

• Protected Mode (with config file):

  {
    "mcpServers": {
      "bigquery": {
        "command": "node",
        "args": [
          "/path/to/your/clone/mcp-bigquery-server/dist/index.js",
          "--project-id",
          "your-project-id",
          "--location",
          "us-central1",
          "--key-file",
          "/path/to/service-account-key.json",
          "--config-file",
          "/path/to/config.json"
        ]
      }
    }
  }
  

## Current Limitations ⚠️

- The configuration examples above are shown for Claude Desktop and Claude Code, but any MCP-compatible client can use this server — provide the same JSON configuration to your AI agent and it will adapt to its own setup
- Queries are read-only with configurable processing limits (set in config.json)
- While both tables and views are supported, some complex view types might have limitations
- A config.json file is optional; without one the server uses safe defaults

## Support & Resources 💬

- 🐛 [Report issues](https://github.com/ergut/mcp-bigquery-server/issues)
- 💡 [Feature requests](https://github.com/ergut/mcp-bigquery-server/issues)
- 📖 [Documentation](https://github.com/ergut/mcp-bigquery-server)

## License 📝

MIT License - See [LICENSE](LICENSE) file for details.

## Author ✍️ 

Salih Ergüt

## Sponsorship

This project is proudly sponsored by:

<div align="center">
  <a href="https://www.oredata.com">
    <img src="assets/oredata-logo-nobg.png" alt="OREDATA" width="300"/>
  </a>
</div>

## Version History 📋

See [CHANGELOG.md](CHANGELOG.md) for updates and version history.