Standard Google Application Default Credentials. Either run `gcloud auth application-default login` once, or set GOOGLE_APPLICATION_CREDENTIALS to a service-account key file path.

Does it surface dry-run cost?

Yes — the dry_run_query tool returns bytes-processed and an estimated cost before you spend credits. Make a habit of running dry_run before query for any new SQL the agent writes.

Can it scope to one dataset?

Yes via the BIGQUERY_DATASET env var; the MCP will only list and query inside that dataset. Combine with an IAM role scoped to that dataset for defence in depth.

Only when explicitly enabled. Default is read-only — the MCP rejects INSERT/UPDATE/DELETE/MERGE unless you opt in.

BigQuery

Name: Top MCPs — curated MCP directory
Creator: Top MCPs
License: https://creativecommons.org/licenses/by/4.0/

Query Google BigQuery datasets from Claude, Cursor, and VS Code.

Score 70(?)CommunityMIT 126Verified May 27, 2026Top MCPs for Databases →

Install View on GitHub

Quick answer

What it does

Connects to Google BigQuery, exposes dataset/table enumeration, schema inspection, dry-run cost estimates, and SELECT execution against your warehouse.

Best for

Schema exploration
Analytical query drafting
Dry-run cost estimation
Cross-dataset joins

Not for

Workloads with strict per-query cost ceilings
Streaming-insert workflows

Setup recipe

Pick your client, then follow the three steps.

Install

claude_desktop_config.jsonjson

{
  "mcpServers": {
    "bigquery": {
      "command": "uvx",
      "args": [
        "mcp-server-bigquery"
      ],
      "env": {
        "GOOGLE_CLOUD_PROJECT": "${GOOGLE_CLOUD_PROJECT}",
        "GOOGLE_APPLICATION_CREDENTIALS": "${GOOGLE_APPLICATION_CREDENTIALS}"
      }
    }
  }
}

Paste under mcpServers. Fully quit and reopen Claude after editing.

CLI or .mcp.jsonshell

# export GOOGLE_CLOUD_PROJECT=my-project
# export GOOGLE_APPLICATION_CREDENTIALS=/path/to/key.json (optional if gcloud auth login)
claude mcp add bigquery -- uvx mcp-server-bigquery

Run from your repo. Commit .mcp.json to share with your team.

.cursor/mcp.jsonjson

{
  "mcpServers": {
    "bigquery": {
      "command": "uvx",
      "args": [
        "mcp-server-bigquery"
      ],
      "env": {
        "GOOGLE_CLOUD_PROJECT": "${GOOGLE_CLOUD_PROJECT}",
        "GOOGLE_APPLICATION_CREDENTIALS": "${GOOGLE_APPLICATION_CREDENTIALS}"
      }
    }
  }
}

Global path: ~/.cursor/mcp.json. Reload window after editing.

.vscode/mcp.jsonjsonc

{
  "servers": {
    "bigquery": {
      "command": "uvx",
      "args": [
        "mcp-server-bigquery"
      ],
      "env": {
        "GOOGLE_CLOUD_PROJECT": "${GOOGLE_CLOUD_PROJECT}",
        "GOOGLE_APPLICATION_CREDENTIALS": "${GOOGLE_APPLICATION_CREDENTIALS}"
      }
    }
  }
}

VS Code uses the "servers" key (not "mcpServers").

~/.codeium/windsurf/mcp_config.jsonjson

{
  "mcpServers": {
    "bigquery": {
      "command": "uvx",
      "args": [
        "mcp-server-bigquery"
      ],
      "env": {
        "GOOGLE_CLOUD_PROJECT": "${GOOGLE_CLOUD_PROJECT}",
        "GOOGLE_APPLICATION_CREDENTIALS": "${GOOGLE_APPLICATION_CREDENTIALS}"
      }
    }
  }
}

Open via Cascade → hammer icon → Configure.

cline_mcp_settings.jsonjson

{
  "mcpServers": {
    "bigquery": {
      "command": "uvx",
      "args": [
        "mcp-server-bigquery"
      ],
      "env": {
        "GOOGLE_CLOUD_PROJECT": "${GOOGLE_CLOUD_PROJECT}",
        "GOOGLE_APPLICATION_CREDENTIALS": "${GOOGLE_APPLICATION_CREDENTIALS}"
      }
    }
  }
}

Open via the Cline sidebar → MCP Servers → Edit.

~/.continue/config.jsonjson

{
  "experimental": {
    "modelContextProtocolServers": [
      {
        "transport": {
          "type": "stdio",
          "command": "uvx",
          "args": [
            "mcp-server-bigquery"
          ],
          "env": {
            "GOOGLE_CLOUD_PROJECT": "${GOOGLE_CLOUD_PROJECT}",
            "GOOGLE_APPLICATION_CREDENTIALS": "${GOOGLE_APPLICATION_CREDENTIALS}"
          }
        }
      }
    ]
  }
}

Continue uses modelContextProtocolServers with a transport block.

~/.codex/config.tomlshell

# ~/.codex/config.toml
[mcp_servers.bigquery]
command = "uvx"
args = [
  "mcp-server-bigquery",
]
env = { GOOGLE_CLOUD_PROJECT = "${GOOGLE_CLOUD_PROJECT}", GOOGLE_APPLICATION_CREDENTIALS = "${GOOGLE_APPLICATION_CREDENTIALS}" }

Codex uses TOML. Each server is a [mcp_servers.<name>] subtable.

~/.config/zed/settings.jsonjsonc

{
  "context_servers": {
    "bigquery": {
      "command": {
        "path": "uvx",
        "args": [
          "mcp-server-bigquery"
        ]
      },
      "env": {
        "GOOGLE_CLOUD_PROJECT": "${GOOGLE_CLOUD_PROJECT}",
        "GOOGLE_APPLICATION_CREDENTIALS": "${GOOGLE_APPLICATION_CREDENTIALS}"
      }
    }
  }
}

Zed calls them "context_servers". Settings live-reload on save.

ChatGPT → Apps directorynone

BigQuery doesn't ship a hosted HTTPS endpoint today. ChatGPT supports remote MCP servers only — to use this server in ChatGPT you'll need to deploy it to a public HTTPS URL first (e.g. via Cloudflare Workers or Vercel) or wait for an official remote build.

2
Set required secrets
Set GOOGLE_CLOUD_PROJECT, GOOGLE_APPLICATION_CREDENTIALS in your shell environment before launching your MCP client.
3
Try a minimum working prompt
Minimum working prompt pending verification. Try any prompt from the MCP’s README once installed.

Tools & permissions

Tool	Description	Args	Side effects
`list_datasets`	List BigQuery datasets in the active project.	—	Read
`list_tables`	List tables in a dataset.	dataset: string	Read
`describe_table`	Return schema + size estimates for a table.	table: string	Read
`dry_run_query`	Return bytes-processed + cost estimate without executing.	sql: string	Read
`query`	Execute a SELECT and return rows.	sql: string	Read

Security & scope

Access scope: Read-only
Sandbox: Authenticates via Google ADC — inherits the active gcloud identity or a service-account key in env. Read-only by default; DML must be explicitly enabled. Combine with IAM roles scoped to the smallest dataset and the smallest project that satisfies the workflow.
Gotchas: BigQuery is billed per byte scanned — always dry_run new SQL before running it.
Service-account keys in env vars persist in client config files. Prefer ADC + short-lived gcloud login.
Read-only is MCP-side, not IAM. Use a viewer-role service account as defence in depth.

Agent prompt pack

— copy into Claude, Cursor, or ChatGPT.

Paste into Claude, Cursor, or ChatGPT. Edit the [brackets] before sending.

Recommend the best MCP servers for [task: e.g. databases work] in [client: Claude].

Constraints:
- Prefer tools that are [official | open-source | read-only] — pick what matters for my use case.
- Exclude MCPs that require [e.g. a paid plan, OAuth-only flows, remote-only transport].
- Return at most 3 picks, ranked.

For each pick include:
1. One-sentence rationale.
2. The ready-to-paste install snippet for my client.
3. Any required secrets I need to create before installing.

Cross-check the top-mcps.com listing: https://top-mcps.com/top-mcps-for-databases

Compare BigQuery against a real alternative. Swap the second MCP in [brackets] if you want a different match.

Compare BigQuery MCP vs [Postgres MCP Pro MCP] for the following job: [describe the job, e.g. "let an agent create GitHub issues on bug triage"].

Judge them on:
- Setup time and complexity (what a new user hits first).
- Auth model (none / API key / OAuth 2.1) and credential risk.
- Transport (stdio / Streamable HTTP / SSE) and where the server runs.
- Required secrets and the blast radius if they leak.
- Operational risk in an unattended agent loop.
- Which one is "good enough" for a weekend prototype vs. production.

End with one sentence: which should I pick for my scenario, which is: [my scenario].

References:
- https://top-mcps.com/mcp/bigquery
- top-mcps.com listing for Postgres MCP Pro

Asks the agent to install and verify. Works inside Claude Code, Cursor Agent, Codex CLI.

Install the BigQuery MCP server for my [client: Claude] at the default config path for that client.

Use the exact install snippet published at https://top-mcps.com/mcp/bigquery (fetch https://top-mcps.com/mcp/bigquery.json for the canonical server.json if you can read URLs).

Before finishing:
1. Create the required secrets (GOOGLE_CLOUD_PROJECT, GOOGLE_APPLICATION_CREDENTIALS) and put them in the appropriate env block — do not hard-code them.
2. Restart or reload the client so it picks up the new server.
3. Verify the server is connected (green / running state) and at least one tool is listed.
4. If anything fails, read the client's MCP logs and report the exact error — do not silently retry.

Confirm when done and list the tools the server now exposes.

Frequently asked questions

What changed

— 2 updates tracked.

May 27, 2026
Refreshed install snippets and fact sheet; verified for 2026.
Jan 30, 2025
Initial directory listing.

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