📌Overview

Purpose: The SQL Analyzer MCP server aims to provide efficient SQL analysis, linting, and dialect conversion to assist users in working with SQL queries effectively.

Overview: The SQL Analyzer MCP server offers a suite of tools for comprehensive SQL query handling, including syntax validation, dialect conversion, and extraction of table and column references. This enables both developers and their AI assistants to manage SQL queries more effectively while ensuring accuracy and compatibility across different database systems.

Key Features:

• SQL Syntax Validation and Linting: Validates SQL queries to identify syntax errors, ensuring user queries are correct and compliant before execution.

• Dialect Conversion: Supports automatic conversion of SQL queries between various database dialects, facilitating seamless transitions and migrations between systems like MySQL and PostgreSQL.

• Table and Column Reference Extraction: Analyzes SQL queries to extract table and column references, aiding in understanding complex queries and enhancing the ability to optimize them.

• Compatibility Checking: Provides information on supported SQL dialects and their features, guiding best practices for specific database systems.

---

mcp-server-sql-analyzer

A Model Context Protocol (MCP) server that provides SQL analysis, linting, and dialect conversion capabilities using SQLGlot.

Overview

The SQL Analyzer MCP server provides tools for analyzing and working with SQL queries. It helps with:

• SQL syntax validation and linting
• Converting queries between different SQL dialects (e.g., MySQL to PostgreSQL)
• Extracting and analyzing table references and dependencies
• Identifying column usage and relationships
• Discovering supported SQL dialects

How Claude Uses This Server

As an AI assistant, this server enhances my ability to help users work with SQL efficiently by:

1. Query Validation: Instantly validate SQL syntax before suggesting it to users, ensuring correct and dialect-appropriate queries.
2. Dialect Conversion: Accurately convert the syntax when users migrate queries between different database systems while preserving query logic.
3. Code Analysis: Table and column reference analysis helps understand complex queries, making it easier to explain query structure and suggest optimizations.
4. Compatibility Checking: Knowing supported dialects and features guides users toward database-specific best practices.

This toolset allows for more accurate and helpful SQL-related assistance while reducing syntax errors or dialect-specific issues.

Tips

Update your personal preferences in Claude Desktop settings to request that generated SQL is first validated using the lint_sql tool.

Tools

1. lint_sql
   Validates SQL query syntax and returns any errors.
   Inputs:

   • sql (string): SQL query to analyze
   • dialect (string, optional): SQL dialect (e.g., 'mysql', 'postgresql')
     Returns:
   • is_valid (boolean): Whether the SQL is valid
   • message (string): Error message or "No syntax errors"
   • position (object, optional): Line and column of error if present

2. transpile_sql
   Converts SQL between different dialects.
   Inputs:

   • sql (string): SQL statement to transpile
   • read_dialect (string): Source SQL dialect
   • write_dialect (string): Target SQL dialect
     Returns:
   • is_valid (boolean): Whether transpilation succeeded
   • message (string): Error message or success confirmation
   • sql (string): Transpiled SQL if successful

3. get_all_table_references
   Extracts table and CTE references from SQL.
   Inputs:

   • sql (string): SQL statement to analyze
   • dialect (string, optional): SQL dialect
     Returns:
   • is_valid (boolean): Whether analysis succeeded
   • message (string): Status message
   • tables (array): List of table references with type, catalog, database, table name, alias, and fully qualified name

4. get_all_column_references
   Extracts column references with table context.
   Inputs:

   • sql (string): SQL statement to analyze
   • dialect (string, optional): SQL dialect
     Returns:
   • is_valid (boolean): Whether analysis succeeded
   • message (string): Status message
   • columns (array): List of column references with column name, table name, and fully qualified name

Resources

SQL Dialect Discovery

dialects://all

Returns a list of all supported SQL dialects for use in all tools.

Configuration

Using uvx (recommended)

Add this to your claude_desktop_config.json:

{
  "mcpServers": {
      "sql-analyzer": {
          "command": "uvx",
          "args": [
              "--from",
              "git+https://github.com/j4c0bs/mcp-server-sql-analyzer.git",
              "mcp-server-sql-analyzer"
          ]
      }
  }
}

Using uv

After cloning this repo, add this to your claude_desktop_config.json:

{
  "mcpServers": {
      "sql-analyzer": {
          "command": "uv",
          "args": [
              "--directory",
              "/path/to/mcp-server-sql-analyzer",
              "run",
              "mcp-server-sql-analyzer"
          ]
      }
  }
}

Development

To run the server in development mode:

# Clone the repository
git clone git@github.com:j4c0bs/mcp-server-sql-analyzer.git

# Run the server
npx @modelcontextprotocol/inspector uv --directory /path/to/mcp-server-sql-analyzer run mcp-server-sql-analyzer

To run unit tests:

uv run pytest .

License

MIT