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mcp-server-sql-analyzer

mcp-server-sql-analyzer

Local
github.com/j4c0bsDatabases32Updated 69w ago

About mcp-server-sql-analyzer

An MCP server that uses SQLGlot to lint SQL, convert queries between dialects, and extract table and column references. It is intended for users who need to validate or migrate SQL queries.

Key features

  1. 01SQL syntax validation with error position
  2. 02Conversion between SQL dialects
  3. 03Table and CTE reference extraction
  4. 04Column reference extraction with table context
  5. 05Listing of supported SQL dialects

Use cases

  1. 01Validating generated SQL before use
  2. 02Migrating a MySQL query to PostgreSQL
  3. 03Analyzing table and column dependencies in complex queries

MCP support

The project is an MCP server that exposes SQL analysis through tools such as lint_sql and transpile_sql, and a dialects://all resource, to MCP clients.

Configuration

Local server config is the same for every client. Copy it into your client’s MCP config file.

mcp.json · Universal config
{
"mcpServers": {
"sql-analyzer": {
"command": "npx",
"args": [
"-y",
"@modelcontextprotocol/inspector"
]
}
}
}
Run commandnpx -y @modelcontextprotocol/inspector

Local servers need a runtime on your machine (e.g. Node.js 18+). Config file locations differ by client; see your client’s docs.

Packages
npm@modelcontextprotocol/inspector

Tools

No tool list yet

Local servers expose tools only once running on your machine, and the maintainer has not provided a tool list.

Details

From the project README

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: I can instantly validate SQL syntax before suggesting it to users, ensuring I provide correct and dialect-appropriate queries.

  2. Dialect Conversion: When users need to migrate queries between different database systems, I can accurately convert the syntax while preserving the query's logic.

  3. Code Analysis: The table and column reference analysis helps me understand complex queries, making it easier to explain query structure and suggest optimizations.

  4. Compatibility Checking: By knowing the supported dialects and their specific features, I can guide users toward database-specific best practices.

This toolset allows me to provide more accurate and helpful SQL-related assistance while reducing the risk of 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
    • Input:
      • sql (string): SQL query to analyze
      • dialect (string, optional): SQL dialect (e.g., 'mysql', 'postgres')
    • Returns: ParseResult containing:
      • 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: TranspileResult containing:
      • 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: TableReferencesResult containing:
      • 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: ColumnReferencesResult containing:
      • 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 [email protected]: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

Comments

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New to MCP?

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An open protocol that defines how AI applications connect to external tools and data in a uniform way. Clients talk to the model; MCP servers provide tools, resources and prompt templates. They exchange standard messages, so one server can be reused by many clients.