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Installation vom Herausgeber. Kein PlugMCP-Endpunkt.
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Auf PlugMCP ausführen
A new server you create. It is not this listing's official endpoint. Use the publisher install when you want their URL and their credentials.
StartenVom Herausgeber
This is a server that lets your LLMs (like Claude) talk directly to your BigQuery data — read-only, with no ability to mutate your warehouse. Think of it as a friendly translator that sits between your AI assistant and your database, making sure they can chat securely and efficiently.
A Model Context Protocol (MCP) server that provides secure, read-only access to BigQuery datasets. Enables Large Language Models (LLMs) to safely query and analyze data through a standardized interface.
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!
This server uses the Model Context Protocol (MCP), which is like a universal translator for AI-database communication. MCP is supported by Claude Desktop, Claude Code, and a growing number of other AI clients.
Here's all you need to do:
SELECT statements are allowed. Every query is validated by BigQuery's own dry-run planner before execution, so INSERT, UPDATE, DELETE, DROP, TRUNCATE, EXPORT DATA, and MERGE are all rejected. The AI agent cannot mutate your warehouse, period.config.json or --maximum-bytes-billed)EXCEPT clauses, so it remains useful without exposing individual records.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.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.
Authenticate with Google Cloud:
gcloud auth application-default login
Add to your MCP client's config (e.g., claude_desktop_config.json for Claude Desktop, .mcp.json for Claude Code):
{
"mcpServers": {
"bigquery": {
"command": "npx",
"args": [
"-y",
"@ergut/mcp-bigquery-server",
"--project-id",
"your-project-id"
]
}
}
}
Start chatting! Open your MCP client and ask questions about your data.
For sensitive data with field-level restrictions:
Authenticate with Google Cloud (choose one method):
gcloud auth application-default login
# 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
Add to your MCP client's config (e.g., claude_desktop_config.json for Claude Desktop, .mcp.json for Claude Code):
With Application Default Credentials:
{
"mcpServers": {
"bigquery": {
"command": "npx",
"args": [
"-y",
"@ergut/mcp-bigquery-server",
"--project-id",
"your-project-id",
"--location",
"us-central1",
"--config-file",
"/path/to/config.json"
]
}
}
}
With a service account key file:
{
"mcpServers": {
"bigquery": {
"command": "npx",
"args": [
"-y",
"@ergut/mcp-bigquery-server",
"--project-id",
"your-project-id",
"--location",
"us-central1",
"--key-file",
"/path/to/service-account-key.json",
"--config-file",
"/path/to/config.json"
]
}
}
}
Start chatting! Open your MCP client and start asking questions about your data.
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.
{
"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
}
--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 a configuration file. If omitted, the server runs in Simple Mode with no protection — there is no implicit default of ./config.json--maximum-bytes-billed: (Optional) Override maximum bytes billed for queries, overrides config.json valueExample 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
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 warehouse, there is no human in the loop to prevent it from reading sensitive columns. A SELECT * FROM patients could expose thousands of PII/PHI records, and the results are then sent to the LLM provider for processing — they leave your network.
This server gives administrators fine-grained control over which columns an AI agent can access. You define preventedFields in config.json and the server blocks queries that would surface those columns into LLM responses. An automated scanner discovers sensitive columns across all your datasets, so coverage stays current as your warehouse grows.
Honest caveat: Field restrictions are cooperative guardrails for AI agents — not a hard SQL firewall against adversarial attackers. See PROTECTION.md for the full threat model.
The server supports three protection modes, set via protectionMode in config.json:
See PROTECTION.md for full configuration, examples, the query pattern reference, scanner setup, and required IAM permissions.
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 point your MCP client config to the local build:
{
"mcpServers": {
"bigquery": {
"command": "node",
"args": [
"/path/to/your/clone/mcp-bigquery-server/dist/index.js",
"--project-id",
"your-project-id",
"--location",
"us-central1"
]
}
}
}
For Protected Mode, add "--config-file", "/path/to/config.json" to the args array (and optionally "--key-file", "/path/to/service-account-key.json" for service account auth).
config.json or via --maximum-bytes-billed)MIT License - See LICENSE file for details.
Salih Ergüt
This project is proudly sponsored by:
See CHANGELOG.md for updates and version history.
A Model Context Protocol (MCP) server that provides secure, read-only access to BigQuery datasets. Enables Large Language Models (LLMs) to safely query and analyze data through a standardized interfac
Übersicht
A Model Context Protocol (MCP) server that provides secure, read-only access to BigQuery datasets. Enables Large Language Models (LLMs) to safely query and analyze data through a standardized interface.
Tools
Noch keine Tool-Liste. Remote-Server erhalten eine nach dem ersten Health-Check.
FAQ
Kopieren Sie auf dieser Seite die stdio-Konfiguration für Cursor, Claude Desktop, Claude Code oder VS Code. Sie nutzt die Installation des Herausgebers, keinen PlugMCP-Proxy.
In diesem Eintrag ist keine erforderliche Umgebungsvariable aufgeführt.
MCP Bigquery Server läuft lokal über stdio.
Zuletzt geprüft: 2026-10-28. A Model Context Protocol (MCP) server that provides secure, read-only access to BigQuery datasets. Enables Large Language Models (LLMs) to safely query and analyze data through a standardized interface.
