Analyzes, optimizes, and suggests indexes for MySQL queries, including complexity scoring, anti-pattern detection, query rewriting, and explain plan visualization.
Analyzes, optimizes, and suggests indexes for PostgreSQL queries, including complexity scoring, anti-pattern detection, query rewriting, and explain plan visualization.
SQL Query Optimizer MCP Server
A powerful Model Context Protocol (MCP) server that analyzes, optimizes, and suggests indexes for SQL queries across multiple dialects (PostgreSQL, MySQL, Oracle, SQL Server). Built with Python and sqlglot.
Features
Advanced Query Analysis
Complexity Scoring: Calculates a heuristic complexity score (1-10) based on joins, subqueries, and set operations.
Detailed Breakdown: Provides a granular breakdown of what contributes to the complexity.
Anti-Pattern Detection: Identifies performance killers like:
SELECT *usageImplicit type casts (e.g.,
id = '123')Potential N+1 queries (LIMIT without ORDER BY)
NULL pitfalls in
NOT INsubqueriesJoin explosions (> 3 joins)
Query Optimization
Automated Rewriting: Uses
sqlglotto apply optimization rules like predicate pushdown and simplification.Alternative Suggestions: Generates alternative query forms (e.g., formatted only, CTE refactoring) alongside the main optimization.
Cost Estimation: Estimates the structural complexity reduction (e.g., "~30%").
DDL Generation: Generates
CREATE INDEXstatements for suggested indexes.
Explain Plan Visualization
ASCII Tree View: Visualizes
EXPLAINoutput as an easy-to-read ASCII tree.Plan Parsing: Extracts scans, costs, and rows from Postgres and MySQL plans.
Index Suggestions
Composite Indexes: Suggests multi-column indexes for
ANDconditions.Covering Indexes: Recommends extending indexes to include selected columns (Index-Only Scans).
Smart Prioritization: Ranks suggestions by impact (Critical, High, Medium, Low).
Installation
Clone the repository:
git clone https://github.com/yourusername/mcp-sql-optimizer.git cd mcp-sql-optimizerCreate a virtual environment:
python -m venv venv source venv/bin/activate # On Windows: venv\Scripts\activateInstall dependencies:
pip install -r requirements.txt
Configuration
Add the server to your MCP client configuration (e.g., claude_desktop_config.json):
Note: On Windows, use double backslashes
π³ Docker (Recommended)
Run the server in a container to avoid environment issues.
Build the image:
docker build -t mcp-sql-optimizer .Configure Claude Desktop:
{ "mcpServers": { "sql-optimizer": { "command": "docker", "args": [ "run", "-i", "--rm", "mcp-sql-optimizer" ] } } }
Usage
The server exposes the following MCP tools:
analyze_query
Analyzes a SQL query for performance issues, complexity, and anti-patterns. Optionally accepts an explain_plan string to visualize the execution plan.
Input:
optimize_query
Rewrites the query to be more performant and provides alternative suggestions.
Input:
suggest_indexes
Suggests indexes to improve query performance, including DDL statements.
Input:
Project Structure
Development
Run the demo client to test features without an MCP client:
Run unit tests:
License
MIT