Google BigQuery, verified
MCP server by Google Cloud · Verified
Lista datasets, inspecciona tablas y ejecuta consultas SQL en proyectos de Google BigQuery, con estado y cancelación de jobs.
Connect to Google BigQuery
Google BigQuery needs sign-in. Connect with its own URL:
Your client opens Google Cloud’s sign-in the first time you use it. The token stays between your client and Google Cloud.
Share this server
Opens this page, with the URL and setup steps.
mcp.tc/i/bigqueryAbout
Conecta un asistente a Google BigQuery. Puede listar datasets y tablas, leer sus metadatos (incluidos los namespaces y tablas de BigLake), ejecutar consultas SQL, obtener resultados paginados y consultar o cancelar jobs de consulta.
Se ejecuta como un endpoint Streamable HTTP alojado en bigquery.googleapis.com/mcp. El acceso usa Google OAuth 2.0 y permisos de IAM en el proyecto de destino. La herramienta SQL de solo lectura se limita a sentencias SELECT, mientras que la herramienta SQL general puede ejecutar sentencias que modifican datos.
What you can do
- Listar IDs de datasets y namespaces de BigLake en un proyecto
- Leer metadatos de datasets y tablas
- Listar IDs de tablas de un dataset
- Ejecutar consultas SELECT de solo lectura
- Ejecutar cualquier sentencia SQL, incluidas escrituras y DDL
- Paginar resultados de jobs de consulta de larga duración
- Consultar el estado de jobs y cancelar jobs en ejecución
Tools 9
list_dataset_idsRead-onlyListar los IDs de datasets de BigQuery y namespaces de BigLake de un proyecto, con paginación.
get_dataset_infoRead-onlyObtener metadatos de un dataset de BigQuery o un namespace de BigLake.
list_table_idsRead-onlyListar los IDs de tablas de un dataset o namespace de BigLake, con paginación.
get_table_infoRead-onlyObtener metadatos de una tabla de BigQuery o una tabla de BigLake.
execute_sql_readonlyRead-onlyEjecutar una consulta SELECT de solo lectura en el proyecto y devolver el resultado.
execute_sqlCan deleteEjecutar cualquier SQL de BigQuery, incluidos INSERT, UPDATE, DELETE, CREATE y funciones de ML.
get_query_resultsRead-onlyConsultar o paginar los resultados de un job de consulta por ID de job.
cancel_jobCan deleteCancelar un job de BigQuery en ejecución.
get_jobRead-onlyObtener el estado, las estadísticas y la configuración de un job de BigQuery.
Read from the live server on 3 Oct 2026. Read-only, Writes and Can delete are hints the server declares; your client decides whether to ask before running a tool.
Example prompts
Lista los datasets de mi proyecto my-analytics-prod
Muestra el esquema de la tabla orders del dataset sales
Ejecuta una consulta de registros diarios de los últimos 30 días
Revisa el estado de mi último job de BigQuery y cancélalo si sigue en ejecución
Set up
Every client connects to bigquery.googleapis.com, and Google Cloud asks you to sign in the first time. Pick yours; the page remembers your choice.
Claude Code
- Run this in a terminal, in your project folder:
claude mcp add --transport http bigquery https://bigquery.googleapis.com/mcp- Start Claude Code, type
/mcp, pick bigquery and choose Authenticate. A browser window opens for the Google Cloud sign-in.
Add --scope user to make it available in every project, not just this one.
Claude Desktop
- Open Settings → Connectors and click Add custom connector.
- Name it Google BigQuery and paste this URL:
https://bigquery.googleapis.com/mcp- Click Add, then Connect, and sign in when Google Cloud asks.
Claude Desktop’s JSON config file only starts local servers. Remote servers go through Connectors, and connectors you add on claude.ai show up here too.
claude.ai
- Open the connector form on claude.ai. This button fills in the name and URL for you:
Add to claude.ai (opens in a new tab)
- Check that the URL reads
https://bigquery.googleapis.com/mcpand click Add. - Click Connect and sign in when Google Cloud asks.
Free plans allow one custom connector. On Team and Enterprise plans an owner adds it under Organization settings → Connectors.
ChatGPT
- On chatgpt.com, open Settings → Security and login and turn on Developer mode.
- Go to
chatgpt.com/pluginsand click + to create an app for a remote MCP server. - Paste
https://bigquery.googleapis.com/mcpas the server URL and choose OAuth. ChatGPT sends you to Google Cloud to sign in.
Developer mode is available on the web for Plus, Pro, Business, Enterprise and Education accounts.
Cursor
Add to Cursor (opens in a new tab)
Or add it by hand to ~/.cursor/mcp.json (all projects) or .cursor/mcp.json (this project):
{
"mcpServers": {
"bigquery": {
"url": "https://bigquery.googleapis.com/mcp"
}
}
}Cursor shows Needs login next to the server. Click it to sign in.
VS Code
Install in VS Code (opens in a new tab)
Or from a terminal:
code --add-mcp '{"name":"bigquery","type":"http","url":"https://bigquery.googleapis.com/mcp"}'Or commit it to the repo in .vscode/mcp.json:
{
"servers": {
"bigquery": {
"type": "http",
"url": "https://bigquery.googleapis.com/mcp"
}
}
}VS Code asks you to sign in the first time the server starts.
Devin Desktop
- Add it to
~/.config/devin/mcp_config.json(macOS and Linux) or%APPDATA%\devin\mcp_config.json(Windows):
{
"mcpServers": {
"bigquery": {
"serverUrl": "https://bigquery.googleapis.com/mcp"
}
}
}- Refresh the MCP server list in Cascade and sign in when asked.
Devin Desktop is the new name for Windsurf. It reads serverUrl (or url) for remote servers.
Codex
codex mcp add bigquery --url https://bigquery.googleapis.com/mcp
codex mcp login bigqueryOr edit ~/.codex/config.toml directly:
[mcp_servers.bigquery]
url = "https://bigquery.googleapis.com/mcp"Gemini CLI
gemini mcp add --transport http bigquery https://bigquery.googleapis.com/mcpThen, inside Gemini CLI, run /mcp auth bigquery to sign in.
This adds it to the current project. Add -s user to use it everywhere.
Any client
Most clients accept this shape. Some name the URL field differently: serverUrl in Devin Desktop, httpUrl in Gemini CLI’s settings file.
{
"mcpServers": {
"bigquery": {
"type": "http",
"url": "https://bigquery.googleapis.com/mcp"
}
}
}Zed puts servers under context_servers in its settings. Cline needs "type": "streamableHttp", or it assumes SSE.
Client only starts local servers? Bridge it with npx -y mcp-remote https://bigquery.googleapis.com/mcp.
FAQ
Can I paste mcp.tc/i/bigquery into my MCP client?
No. mcp.tc links are pages, not server addresses. Connect with https://bigquery.googleapis.com/mcp, so your client talks to Google BigQuery directly. The sign-in token only works there anyway. The quick link is for sharing: it opens this page, with setup steps for every client.
Does Google BigQuery need an API key or a sign-in?
Yes, you need a Google Cloud account. The first time your client connects, it opens Google Cloud’s sign-in page. The token goes straight to the server’s own URL, never through mcp.tc.
Is Google BigQuery a remote or a local server?
Remote. Google Cloud hosts it at https://bigquery.googleapis.com/mcp, and it speaks Streamable HTTP. There’s nothing to install.
What can Google BigQuery do?
It has 9 tools, including list_dataset_ids, get_dataset_info and list_table_ids. You can listar IDs de datasets y namespaces de BigLake en un proyecto, leer metadatos de datasets y tablas and listar IDs de tablas de un dataset.
Which clients can use it?
Any client that supports remote MCP servers: Claude Code, Claude Desktop, claude.ai, ChatGPT in developer mode, Cursor, VS Code, Devin Desktop, Codex, Gemini CLI, Zed and others. The setup steps cover each one.
Who wrote this page?
mcp.tc’s robot read Google BigQuery’s own metadata (its MCP handshake, tool list and public pages), and an AI model drafted the text from it. A person reviews anything the checks can’t confirm. The text can still be wrong, so if you spot a mistake, use Report this listing on this page. This translation was made by an AI model from the English text.
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https://mcp.tc/i/bigquery.jsonmcp.tc isn’t affiliated with Google Cloud. This page was built from Google BigQuery’s public metadata, last checked on 3 Oct 2026, and an AI model wrote the description, so it can be wrong. Names and marks belong to their owners. Something off? Report this listing. The translation was made by an AI model from the English text.