Best MCP Servers for Databases in 2026 (Postgres, Snowflake, BigQuery)

The best MCP servers for databases in 2026 are the ones maintained by the database vendors themselves: Supabase, MongoDB, Neon and ClickHouse for self-managed setups, and the managed MCP servers from Snowflake, BigQuery and Databricks for data warehouses. If you need one server across several databases, DBHub and Google's MCP Toolbox for Databases are the two most used options.

The more important decision is not which server, but how it is locked down. An MCP server gives an AI model the ability to run queries, and several popular database servers have shipped read-only modes that could be bypassed. This guide compares the main options on who maintains them, which databases they support, whether they default to read-only, and how they are hosted. All details were checked on 2026-09-30.

At a glance

First, avoid the archived reference servers

The original Postgres and SQLite servers from the Model Context Protocol project now sit in an archived repository with no security guarantees. Both had published SQL injection issues: Datadog showed the Postgres server's read-only wrapper could be bypassed, and Trend Micro reported an injection flaw in the SQLite server, which had been forked thousands of times. Some "best of" lists still recommend them. Do not use them for anything real.

The pattern repeated in 2026. AWS's Postgres MCP server had a critical read-only bypass, fixed in version 1.1.7. The lesson from all of these is the same, and it runs through the rest of this list: treat a server's read-only flag as a convenience, and enforce read-only access with a database role that cannot write. Our MCP security guide covers the broader risks.

The best MCP servers for databases

1. Supabase MCP

Supabase's server is tied with MongoDB as the most recommended database MCP server across the six roundups we reviewed. It runs as a hosted endpoint with OAuth sign-in, and can be limited to one project, a set of features, or read-only mode. It is still pre-1.0, and Supabase itself advises using it against development data rather than production, because of prompt-injection risk, a risk Simon Willison illustrated with a worked example.

Best for: teams building on Supabase who want an AI assistant in the development loop.

2. MongoDB MCP Server

The official MongoDB server covers self-managed MongoDB and Atlas. It has the most granular safety controls on this list: a read-only flag, the ability to disable specific tools, and a list of tools that require confirmation before running. Note that read-only is off by default, which some roundups get wrong.

Best for: MongoDB shops that want fine-grained control over what the model can run.

3. Neon MCP

Neon's server is remote with OAuth and supports read and write scopes plus a read-only mode. Neon's own documentation positions it for development workflows rather than production databases.

Best for: teams on Neon who want branch-based experimentation with AI help.

4. ClickHouse MCP

The ClickHouse server is one of the few that is read-only by default, which makes it a sensible pick for analytics workloads where the model should only ever read.

Best for: analytics teams on ClickHouse.

5. Snowflake managed MCP

Snowflake now offers a managed MCP server with OAuth and role-based access control, and its SQL tool is read-only by default. The older Snowflake Labs server that several lists still call "official" has been deprecated in favor of this one.

Best for: Snowflake customers who want governance handled inside Snowflake.

6. BigQuery MCP

Google's BigQuery MCP is remote and turned on with the BigQuery API. It includes a read-only SQL tool with a three-minute query limit and a 3,000-row cap, sensible guardrails for an AI client.

Best for: Google Cloud teams who want warehouse access without running a server.

7. Databricks managed MCP

Databricks offers managed MCP servers in public preview, governed by Unity Catalog and billed through the underlying compute. None of the roundups we reviewed include it, but it is the natural choice for Databricks customers.

Best for: teams whose data already lives in Databricks with Unity Catalog permissions.

8. Google MCP Toolbox for Databases

MCP Toolbox is the most starred database MCP project on this list and supports about 20 databases, including Postgres, MySQL, MongoDB, Snowflake and ClickHouse. You define tools in a configuration file, which gives control but is more setup than a single-database server needs.

Best for: platform teams standardizing many databases behind one server.

9. DBHub

DBHub from Bytebase connects to Postgres, MySQL, MariaDB, SQL Server, Oracle and SQLite, with a read-only mode, row limits and query timeouts. It has no built-in access control or audit log, so it fits individual developers better than shared team use.

Best for: developers who work across several SQL databases.

10. Postgres MCP Pro

Postgres MCP Pro goes beyond running queries, with index tuning and health checks, and offers a restricted mode for safer use.

Best for: engineers doing Postgres performance work with an AI assistant.

11. Coworker

Every server above connects a model to one database. Coworker takes a different approach: it connects your databases alongside the rest of your company's tools, so answers can combine a Postgres table or a Snowflake query with context from Salesforce, Slack or Jira.

Coworker connects to PostgreSQL (including RDS, Supabase and Neon) and Snowflake with read-only access only, and also supports BigQuery and Databricks. Agents can query on a schedule, from hourly to weekly, and post results or threshold alerts to Slack or email. Through Coworker MCP, Claude, Cursor and other MCP clients use your existing Coworker login and SSO, search tools are retrieval-only, and you can trigger Coworker agents directly from the client. Book a demo to see it on your own databases.

Where it is not the right fit: if you want an AI assistant to write schema migrations or tune indexes, a single-database server like Postgres MCP Pro or your database vendor's own server is the better tool.

Best for: teams who want database answers in the context of the rest of the business, without giving a model write access.

How to choose a database MCP server

1. Start with your database vendor's official server. Official servers are maintained alongside the database and are where security fixes land first.
2. Enforce read-only at the database. Create a role with SELECT-only grants and connect with it. Do not rely only on a server flag.
3. Prefer managed servers for production data. Snowflake, BigQuery and Databricks keep authentication and permissions inside the platform you already govern.
4. Keep development and production separate. Supabase and Neon both recommend their servers for development data.
5. Decide whether you need one database or business context. For one database, pick its server. For questions that span your data and your business tools, a platform like Coworker avoids stitching several servers together.

For a wider framework, see how to choose MCP servers for your team and our guide to running MCP safely in the enterprise.

Frequently asked questions

What is the best MCP server for Postgres?

For Supabase or Neon databases, use the vendor's official server. For self-hosted or cloud Postgres, DBHub and Postgres MCP Pro are the most used community options. Avoid the archived Postgres reference server, which had a published read-only bypass.

Are database MCP servers safe to use in production?

They can be, if access is enforced at the database. Several servers have shipped read-only modes that were bypassed, so connect with a database role that has read-only grants, prefer managed servers for production, and keep write access out of AI clients entirely.

Is there an official Snowflake MCP server?

Yes. Snowflake offers a managed MCP server with OAuth and role-based access control, and its SQL tool is read-only by default. The older Snowflake Labs server has been deprecated in its favor.

Does BigQuery have an MCP server?

Yes. Google provides a remote BigQuery MCP server that you enable with the BigQuery API. It includes a read-only SQL tool with a three-minute query limit and a 3,000-row result cap.

Can one MCP server connect to multiple databases?

Yes. Google's MCP Toolbox supports about 20 databases and DBHub covers six SQL databases. Coworker connects Postgres, Snowflake, BigQuery and Databricks alongside 50+ business apps, and MCP clients reach it through Coworker MCP.

What is the difference between an MCP server and a database connector?

A database MCP server exposes query tools directly to an AI client, which then decides what to run. A connector in a platform like Coworker gives the platform's agents read-only access to the database and lets them combine query results with data from your other tools, so the AI client never holds database credentials itself.

Related reading

• MCP security: risks and mitigations
• Enterprise MCP: deploying MCP safely
• How to choose MCP servers for your team
• MCP tools: the complete guide
• Claude Code MCP servers
• What is an MCP gateway?