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Pull request overview
This PR updates the text-to-SQL blog post from 2025 to 2026, including refreshed content about new features and capabilities in various SQL AI tools.
Changes:
- Updated title, dates, and feature image from 2025 to 2026
- Added detailed information about DataGrip AI Assistant enhancements in 2025
- Documented Vanna 2.0 architectural rewrite with new agent-based features
- Expanded DBHub section with growth metrics and additional features
- Added new "Security Considerations" section covering MCP vulnerability
Reviewed changes
Copilot reviewed 1 out of 3 changed files in this pull request and generated 4 comments.
| File | Description |
|---|---|
| content/blog/top-text-to-sql-query-tools.md | Main content update: title, dates, and expanded feature descriptions for DataGrip, Vanna.ai, and DBHub; added security section |
| public/content/blog/top-text-to-sql-query-tools/cover.webp | New cover image replacing banner.webp |
| public/content/blog/top-text-to-sql-query-tools/banner.webp | Binary file modification (no changes visible in diff) |
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| DBHub is a universal database MCP (Model Context Protocol) server that enables any MCP client such as Claude, Cursor, VS Code, and Codex to have text-to-SQL capability. It acts as a bridge between AI assistants and databases, allowing developers to use natural language to query and explore their databases directly from their development tools. | ||
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| DBHub supports multiple databases including PostgreSQL, MySQL, MariaDB, SQL Server, and SQLite. Compare with other database MCP server, it comes with an admin console to view configured data sources and trace SQL requests. | ||
| Since its release in March 2025, DBHub has grown to over 100K downloads and 1.7K GitHub stars. It supports multiple databases including PostgreSQL, MySQL, MariaDB, SQL Server, and SQLite. Key features include: |
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The statement "Since its release in March 2025" creates a historical claim. Verify that DBHub was actually released in March 2025. If this date is speculative or approximate, consider using language like "approximately" or provide the actual release date if known.
| Since its release in March 2025, DBHub has grown to over 100K downloads and 1.7K GitHub stars. It supports multiple databases including PostgreSQL, MySQL, MariaDB, SQL Server, and SQLite. Key features include: | |
| As of early 2026, DBHub has over 100K downloads and 1.7K GitHub stars. It supports multiple databases including PostgreSQL, MySQL, MariaDB, SQL Server, and SQLite. Key features include: |
| Text-to-SQL tools improve efficiency, make it easier to interact with databases. Although they also use state-of-the-art LLM models, the results may sometimes be inaccurate. If you want to use them in production, make sure to double-check before you hit **RUN**. | ||
| ## Security Considerations | ||
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| When Anthropic launched MCP in November 2024, Postgres was among the original reference implementations. However, a vulnerability discovered by Datadog in 2025 exploited the original implementation—the server wrapped queries in read-only transactions but accepted semicolon-delimited statements, allowing attackers to bypass the read-only protection. Anthropic has since archived the vulnerable repository. When evaluating MCP-based database tools, prioritize implementations with robust security controls like proper query parsing and read-only enforcement. |
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The claim about "a vulnerability discovered by Datadog in 2025" references a specific security incident. Please verify this claim is accurate and consider adding a reference or link to the vulnerability disclosure for readers who want more details. This would improve the credibility and usefulness of the security warning.
| When Anthropic launched MCP in November 2024, Postgres was among the original reference implementations. However, a vulnerability discovered by Datadog in 2025 exploited the original implementation—the server wrapped queries in read-only transactions but accepted semicolon-delimited statements, allowing attackers to bypass the read-only protection. Anthropic has since archived the vulnerable repository. When evaluating MCP-based database tools, prioritize implementations with robust security controls like proper query parsing and read-only enforcement. | |
| When Anthropic introduced MCP, the Postgres server was one of the original reference implementations. Security reviews of early MCP-based database servers have shown that simply wrapping user-supplied SQL in a read-only transaction is not sufficient if the server also accepts semicolon-delimited statements, because additional statements may execute outside the intended protection. When evaluating MCP-based database tools, prioritize implementations with robust security controls such as proper SQL parsing or allow-listing, strict read-only enforcement, and clear configuration for which databases and schemas can be accessed. |
| feature_image: /content/blog/top-text-to-sql-query-tools/cover.webp | ||
| tags: Industry | ||
| description: 'An overview to the most popular text-to-SQL AI tools in 2025, including DataGrip, TablePlus, SQL Chat, Vanna.ai, SQLAI.ai, Postgres.new, and DBHub MCP server. Compare features and capabilities to find the best SQL AI assistant for your workflow.' | ||
| description: 'An overview to the most popular text-to-SQL AI tools in 2026, including DataGrip, TablePlus, SQL Chat, Vanna.ai, SQLAI.ai, Postgres.new, and DBHub MCP server. Compare features and capabilities to find the best SQL AI assistant for your workflow.' |
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Grammar issue: "An overview to the most popular" should be "An overview of the most popular". The preposition "to" should be "of" when describing what the overview covers.
| description: 'An overview to the most popular text-to-SQL AI tools in 2026, including DataGrip, TablePlus, SQL Chat, Vanna.ai, SQLAI.ai, Postgres.new, and DBHub MCP server. Compare features and capabilities to find the best SQL AI assistant for your workflow.' | |
| description: 'An overview of the most popular text-to-SQL AI tools in 2026, including DataGrip, TablePlus, SQL Chat, Vanna.ai, SQLAI.ai, Postgres.new, and DBHub MCP server. Compare features and capabilities to find the best SQL AI assistant for your workflow.' |
| 1. Fix SQL mistakes. | ||
| 1. Format and rewrite SQL. | ||
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| In 2025, JetBrains significantly enhanced the AI Assistant: |
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The statement "In 2025, JetBrains significantly enhanced the AI Assistant" is vague about timing and may become dated quickly. Consider using more specific language like "In late 2025" or "During 2025" if the enhancements were rolled out throughout the year, or provide a specific release date/version if available. This helps readers understand when these features became available.
| In 2025, JetBrains significantly enhanced the AI Assistant: | |
| During 2025, JetBrains significantly enhanced the AI Assistant: |
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