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MCP Database Performance Analyzer

by prateeik
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TypeScript
Remote

MCP Database Performance Analyzer

Autonomous MCP server for SQL schema analysis, data generation, benchmarking and optimization.

A production-ready Model Context Protocol (MCP) server that acts as an intelligent SQL performance analysis and optimization assistant. Point it at a SQL dump, and it will parse the schema, populate a database with realistic relational data at scale, benchmark queries, identify bottlenecks, explain root causes, and generate optimized SQL and indexing recommendations.

✨ Features

Category

Capabilities

Schema Analysis

Parse SQL dumps · Detect PK/FK/indexes/views/procedures/triggers · Relationship graph · Missing FK indexes

Data Generation

6 business domains · 10k–10M rows · Referential integrity · Deterministic seed

Query Execution

SELECT/JOIN/CTE/Window functions · Execution metrics · EXPLAIN analysis

Benchmarking

P50/P95/P99 latencies · QPS · Concurrent workloads · Before/after comparison

Optimization

Anti-pattern detection · Query rewriting · Index recommendations · Health score (0–100)

Reporting

JSON · Markdown · HTML · CSV · PDF · ERD diagrams

Related MCP server: MCP Database Server

🚀 Quick Start

Prerequisites

  • Node.js 20+

  • MySQL 8.0 (or use Docker)

Option 1: Docker (Recommended)

# Start MySQL + MCP server
docker compose up -d

# Wait for MySQL to be healthy
docker compose logs -f mcp-server

Option 2: Local

# Install dependencies
npm install

# Copy and configure environment
cp .env.example .env
# Edit .env with your MySQL credentials

# Build
npm run build

# Run
npm start

🔧 MCP Tools

Tool

Description

load_schema

Parse a SQL dump file and extract the full schema model

analyze_schema

Analyze relationships, ERD, missing FK indexes, circular dependencies

create_database

Execute all DDL in dependency order and validate creation

generate_test_data

Preview generated data without inserting (dry run)

populate_database

Insert realistic relational data at specified scale

execute_query

Execute a SQL query with metrics capture

benchmark_query

Benchmark a query with P50/P95/P99 and QPS

benchmark_database

Run a mixed workload simulation

analyze_execution_plan

EXPLAIN + EXPLAIN ANALYZE with insights

detect_slow_queries

Classify queries and detect root causes

recommend_indexes

Detect missing, duplicate, unused, redundant indexes

optimize_query

Anti-pattern detection + query rewriting

compare_queries

Side-by-side benchmark with improvement %

generate_report

Full report in JSON/Markdown/HTML/CSV/PDF

reset_database

Truncate or recreate the database

📋 Example Workflow

1. load_schema(sql: "<content of dump.sql>")
2. analyze_schema()
3. create_database()
4. populate_database(domain: "ecommerce", datasetSize: "100k")
5. benchmark_query(sql: "SELECT * FROM orders JOIN customers ON ...")
6. analyze_execution_plan(sql: "...")
7. detect_slow_queries(queries: [...])
8. recommend_indexes()
9. optimize_query(sql: "...")
10. generate_report(formats: ["html", "pdf"])

🗄 Database Support

Database

Status

MySQL 8.0

✅ Full support (primary)

PostgreSQL

🔶 Stub (core ops work, advanced metrics pending)

SQLite

✅ Full support (used for tests)

SQL Server

📋 Planned

🏢 Business Domains

The data generator supports 6 business domain profiles:

  • ecommerce — customers, products, orders, payments, reviews

  • erp — suppliers, purchase orders, inventory, warehouses, accounts

  • crm — contacts, leads, deals, activities, campaigns

  • hospital — patients, doctors, appointments, prescriptions, diagnoses

  • banking — accounts, transactions, loans, cards, branches

  • hr — employees, departments, positions, payroll, performance reviews

🔍 Optimization Detectors

Anti-Pattern

Detection

Auto-Fix

SELECT *

✅

✅

Large OFFSET pagination

✅

✅

Non-sargable predicates

✅

✅

Leading wildcard LIKE

✅

—

Implicit type cast

✅

✅

Missing FK indexes

✅

✅

Duplicate indexes

✅

✅

Unused indexes

✅

✅

Full table scans

✅

—

Filesort

✅

—

Temporary tables

✅

—

📊 Health Score

The database health score (0–100, Grade A–F) is computed from weighted categories:

Category

Weight

Index Strategy

30%

Schema Design

25%

Query Efficiency

25%

Data Distribution

10%

Security

10%

🧪 Testing

# Unit tests (no external DB required)
npm test

# Integration tests (requires MySQL or use SQLite path)
npm run test:integration

# Type check
npm run typecheck

📁 Project Structure

src/
├── index.ts              # Entry point
├── server.ts             # MCP server + adapter setup
├── config/               # Typed configuration
├── adapters/             # Database adapters (MySQL, PostgreSQL, SQLite)
├── modules/
│   ├── sql-parser/       # SQL DDL/DML parser
│   ├── schema-analyzer/  # Relationship graph, orphan/cycle detection
│   ├── erd-generator/    # Mermaid ERD generation
│   ├── database-creator/ # DDL execution in dependency order
│   ├── data-generator/   # Faker-based relational data generation
│   ├── bulk-loader/      # Batched INSERT with concurrency control
│   ├── query-executor/   # Query execution with metrics
│   ├── benchmark-engine/ # P50/P95/P99 latency benchmarking
│   ├── explain-analyzer/ # EXPLAIN plan parsing and insights
│   ├── slow-query-detector/ # Query classification
│   ├── index-analyzer/   # Missing/duplicate/unused index detection
│   ├── optimization-engine/ # Anti-patterns, query rewriting, health score
│   └── report-generator/ # JSON/MD/HTML/CSV/PDF rendering
├── tools/                # All 15 MCP tool handlers
├── types/                # Shared TypeScript interfaces
└── utils/                # Logger, progress reporter, retry

🌐 Claude Desktop Integration

Add to your claude_desktop_config.json:

{
  "mcpServers": {
    "db-optimizer": {
      "command": "node",
      "args": ["/path/to/database-benchmark/dist/index.js"],
      "env": {
        "DB_ADAPTER": "mysql",
        "DB_HOST": "127.0.0.1",
        "DB_PORT": "3306",
        "DB_USER": "root",
        "DB_PASSWORD": "root",
        "DB_NAME": "mcp_benchmark"
      }
    }
  }
}

🤝 Contributing

Contributions are welcome! Please see our Contributing Guide for guidelines on code style, testing, and submitting pull requests.

📄 License

This project is licensed under the terms of the MIT License.

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