This is a Python-based MCP (Model Context Protocol) server that retrieves dataset, table, and schema information from Google Cloud BigQuery, caches
Bq MCP MCP server exists for a simple reason — assistants are far more useful when they can act on Bq MCP directly instead of describing what you should do. This is a Python-based MCP (Model Context Protocol) server that retrieves dataset, table, and schema information from Google Cloud BigQuery, caches it locally, and serves it via MCP. Its primary purpose is to enable generative AI systems.
Installation goes through your MCP client rather than a global install: point it at bq_mcp_server on PyPI and it is fetched when the client starts. The copy-paste blocks for Claude Desktop, Claude Code and Cursor are further down this page.
Once Bq MCP is connected, these are the calls the assistant has available:
save_query_result — The save_query_result tool provides advanced query execution with file export capabilities:Prerequisites — The Prerequisites tool exposed by this serverAmong the database access options, the useful question is rarely "what can it do" but "what does it cost you to run" — permissions, credentials, and how much of your context its toolset consumes. Bq MCP's toolset — save_query_result, Prerequisites — is a fair guide to whether it matches your workflow. It is maintained by takada-at; worth a glance at recent repository activity before you build anything load-bearing on it.
SyncDev reviews every entry in this directory against the project's own documentation before publishing, and revisits them as servers change.
| Tool | What it does |
|---|---|
| save_query_result | The save_query_result tool provides advanced query execution with file export capabilities: |
| Prerequisites | The Prerequisites tool exposed by this server. |
JSON
```json
{
"mcpServers": {
"bq_mcp_server": {
"command": "uvx",
"args": [
"--from",
"git+https://github.com/takada-at/bq_mcp_server",
"bq_mcp_server",
"--project-ids",
"<your project ids>"
]
}
}
}Configuration as documented by the project. Restart the client after saving.
Read-only SQL access to Postgres — let your assistant inspect schemas and answer questions from real data.
Manage your whole Supabase project in conversation — database, auth, storage, Edge Functions and branches.
Query, modify and analyse local SQLite databases in conversation — the fastest way to chat with a data file.
Metabase ships its own MCP endpoint — search your BI content, build and run queries, and save questions and dashboards without leaving the chat.
Official MongoDB server covering data, schemas and Atlas management — from find queries to spinning up clusters.
Serverless Postgres with database branching — point your assistant at Neon and let it work on disposable copies.