mcp-db-analyzer

mcp-db-analyzer

A Model Context Protocol (MCP) server that gives AI assistants deep visibility into databases, inspecting schemas, detecting index problems, analyzing table bloat, and explaining query plans across PostgreSQL, MySQL, and SQLite.

Category
Visit Server

README

npm version License: MIT

MCP DB Analyzer

A Model Context Protocol (MCP) server that gives AI assistants deep visibility into your databases. It inspects schemas, detects index problems, analyzes table bloat/fragmentation, and explains query plans — so your AI can give you actionable database optimization advice instead of generic suggestions.

Supports PostgreSQL, MySQL, and SQLite.

Why This Tool?

There are dozens of database MCP servers — most are CRUD gateways (run queries, list tables). This tool analyzes your database: schema problems, missing indexes, bloated tables, slow queries, vacuum health.

Other analytical MCP servers (CrystalDBA, pg-dash, MCP-PostgreSQL-Ops) cover PostgreSQL only. MCP DB Analyzer is the only analytical MCP server that supports PostgreSQL, MySQL, and SQLite in a single npx install — no Python, no Go, no Docker.

Features

  • 9 MCP tools for comprehensive database analysis
  • PostgreSQL + MySQL + SQLite support via --driver flag
  • Read-only by design — all queries wrapped in READ ONLY transactions
  • Markdown output optimized for LLM consumption
  • Zero configuration — just set DATABASE_URL

Pro Tier

Generate exportable diagnostic reports (HTML + PDF) with a Pro license key.

  • Full JVM thread dump analysis report with actionable recommendations
  • PDF export for sharing with your team
  • Priority support

<!-- TODO: replace placeholder Stripe Payment Link once STRIPE_SECRET_KEY is configured --> $9.00/monthGet Pro License

Pro license key activates the generate_report MCP tool in mcp-jvm-diagnostics.

Installation

npx mcp-db-analyzer

Or install globally:

npm install -g mcp-db-analyzer

Configuration

Set the DATABASE_URL environment variable:

export DATABASE_URL="postgresql://user:password@localhost:5432/mydb"

Or use individual PG variables: PGHOST, PGPORT, PGDATABASE, PGUSER, PGPASSWORD.

MySQL

Set DATABASE_URL with a MySQL connection string and pass --driver mysql:

export DATABASE_URL="mysql://user:password@localhost:3306/mydb"
mcp-db-analyzer --driver mysql

Or use individual MySQL variables: MYSQL_HOST, MYSQL_PORT, MYSQL_DATABASE, MYSQL_USER, MYSQL_PASSWORD.

You can also set DB_DRIVER=mysql as an environment variable instead of passing the flag.

SQLite

Pass a file path via DATABASE_URL and use --driver sqlite:

export DATABASE_URL="/path/to/database.db"
mcp-db-analyzer --driver sqlite

Claude Desktop (PostgreSQL)

Add to ~/.claude/claude_desktop_config.json:

{
  "mcpServers": {
    "db-analyzer": {
      "command": "npx",
      "args": ["-y", "mcp-db-analyzer"],
      "env": {
        "DATABASE_URL": "postgresql://user:password@localhost:5432/mydb"
      }
    }
  }
}

Claude Desktop (MySQL)

{
  "mcpServers": {
    "db-analyzer": {
      "command": "npx",
      "args": ["-y", "mcp-db-analyzer", "--driver", "mysql"],
      "env": {
        "DATABASE_URL": "mysql://user:password@localhost:3306/mydb"
      }
    }
  }
}

Claude Desktop (SQLite)

{
  "mcpServers": {
    "db-analyzer": {
      "command": "npx",
      "args": ["-y", "mcp-db-analyzer", "--driver", "sqlite"],
      "env": {
        "DATABASE_URL": "/path/to/database.db"
      }
    }
  }
}

Quick Demo

Once configured, try these prompts in Claude:

  1. "Show me the schema and how tables are related" — Returns table structures, foreign keys, and identifies orphan tables
  2. "Are there any slow queries or missing indexes?" — Ranks slow queries by execution time and suggests indexes to add
  3. "How many connections are active? Are any queries blocked?" — Shows connection pool utilization, idle-in-transaction sessions, and blocked queries

Tools

inspect_schema

List all tables with row counts and sizes, or drill into a specific table's columns, types, constraints, and foreign keys.

Parameters:

  • table (optional) — Table name to inspect. Omit to list all tables.
  • schema (default: "public") — Database schema.
> inspect_schema

## Tables in schema 'public'

| Table       | Rows (est.) | Total Size |
|-------------|-------------|------------|
| users       | 12,450      | 3.2 MB     |
| orders      | 89,100      | 18.4 MB    |
| order_items | 245,000     | 12.1 MB    |
> inspect_schema table="users"

## Table: public.users

- **Rows (est.)**: 12,450
- **Total size**: 3.2 MB

### Columns
| # | Column | Type          | Nullable | Default |
|---|--------|---------------|----------|---------|
| 1 | id     | integer       | NO       | nextval |
| 2 | email  | varchar(255)  | NO       | -       |
| 3 | name   | varchar(100)  | YES      | -       |

analyze_indexes

Find unused indexes wasting disk space and missing indexes causing slow sequential scans. Also detects unindexed foreign keys.

Parameters:

  • schema (default: "public") — Database schema.
  • mode ("usage" | "missing" | "all", default: "all") — Analysis mode.
> analyze_indexes

### Unused Indexes (2 found)
| Table | Index              | Size   | Definition                    |
|-------|--------------------|--------|-------------------------------|
| users | idx_users_legacy   | 1.2 MB | CREATE INDEX ... (old_col)    |

### Unindexed Foreign Keys (1 found)
| Table       | Column  | FK →   | Constraint        |
|-------------|---------|--------|-------------------|
| order_items | user_id | users  | fk_items_user_id  |

explain_query

Run EXPLAIN on a SQL query and get a formatted execution plan with cost estimates, node types, and optimization warnings. Optionally run EXPLAIN ANALYZE for actual timing (SELECT queries only).

Parameters:

  • sql — The SQL query to explain.
  • analyze (default: false) — Run EXPLAIN ANALYZE (executes the query; SELECT only).
> explain_query sql="SELECT * FROM orders WHERE status = 'pending'"

## Query Plan Analysis

- **Estimated Total Cost**: 1234.56
- **Estimated Rows**: 500

### Plan Tree
→ Seq Scan on orders (cost=0..1234.56 rows=500)
  Filter: (status = 'pending')

### Potential Issues
- **Sequential Scan** on `orders` (~500 rows). Consider adding an index.

analyze_table_bloat

Analyze table bloat by checking dead tuple ratios, vacuum history, and table sizes. Recommends VACUUM ANALYZE for tables with >10% dead tuples.

Parameters:

  • schema (default: "public") — Database schema.
> analyze_table_bloat

### Tables Needing VACUUM (1 found)
| Table     | Live Tuples | Dead Tuples | Bloat % | Size  | Last Vacuum |
|-----------|-------------|-------------|---------|-------|-------------|
| audit_log | 8,000       | 2,000       | 20.0%   | 10 MB | Never       |

### Recommended Actions
VACUUM ANALYZE public.audit_log;

suggest_missing_indexes

Find tables with high sequential scan counts and zero index usage, cross-referenced with unused indexes wasting space. Provides actionable CREATE INDEX and DROP INDEX recommendations.

Parameters:

  • schema (default: "public") — Database schema.
> suggest_missing_indexes

### Tables Missing Indexes (1 found)
| Table  | Seq Scans | Index Scans | Rows   | Size  |
|--------|-----------|-------------|--------|-------|
| events | 5,000     | 0           | 50,000 | 25 MB |

### Unused Indexes (1 found)
| Table | Index            | Size | Definition                       |
|-------|------------------|------|----------------------------------|
| users | idx_users_legacy | 8 kB | CREATE INDEX ... (legacy_col)    |

DROP INDEX public.idx_users_legacy;

analyze_slow_queries

Find the slowest queries using pg_stat_statements (PostgreSQL) or performance_schema (MySQL). Shows execution times, call counts, and identifies optimization candidates.

Parameters:

  • schema (default: "public") — Database schema.
  • limit (default: 10) — Number of slow queries to return.
> analyze_slow_queries

## Slow Query Analysis (by avg execution time)

| # | Avg Time | Total Time | Calls | Avg Rows | Query |
|---|----------|------------|-------|----------|-------|
| 1 | 150.0ms  | 750000ms   | 5000  | 5        | `SELECT * FROM orders WHERE status = $1` |
| 2 | 200.0ms  | 40000ms    | 200   | 2        | `SELECT u.* FROM users u JOIN orders o...` |

### Recommendations
- **2 high-impact queries** — called >100 times with >100ms avg
- **2 queries returning few rows but slow** — likely missing indexes

analyze_connections

Analyze active database connections. Detects idle-in-transaction sessions, long-running queries, lock contention, and connection pool utilization. PostgreSQL and MySQL only.

> analyze_connections

## Connection Analysis (PostgreSQL)

### Connection States
| State | Count |
|-------|-------|
| active | 3 |
| idle | 12 |
| idle in transaction | 2 |
| **Total** | **17** |

**Max connections**: 100
**Utilization**: 17.0%

### Idle-in-Transaction Connections
| PID  | User | Duration | Query |
|------|------|----------|-------|
| 1234 | app  | 00:05:30 | UPDATE orders SET status = $1 |

analyze_table_relationships

Analyze foreign key relationships between tables. Builds a dependency graph showing entity connectivity, orphan tables (no FKs), cascading delete chains, and hub entities.

Parameters:

  • schema (default: "public") — Database schema.
> analyze_table_relationships

## Table Relationships

**Tables**: 5
**Foreign Keys**: 4

### Entity Connectivity
| Table | Incoming FKs | Outgoing FKs | Total |
|-------|-------------|-------------|-------|
| users **hub** | 5 | 0 | 5 |
| orders | 1 | 2 | 3 |

### Orphan Tables (no FK relationships)
- `audit_log`

### Cascading Delete Chains
- **users** → cascades to: orders, addresses
  - **orders** → further cascades to: order_items

analyze_vacuum

Analyze PostgreSQL VACUUM maintenance status. Checks dead tuple ratios, vacuum staleness, autovacuum configuration, and identifies tables needing manual VACUUM. PostgreSQL only.

> analyze_vacuum

Detects:

  • Tables with high dead tuple ratios (>10% warning, >20% critical)
  • Tables never vacuumed or analyzed
  • Autovacuum disabled globally
  • Autovacuum configuration issues

Output includes:

  • Findings grouped by severity (CRITICAL / WARNING / INFO)
  • Tables needing VACUUM with dead tuple percentages
  • Full vacuum history per table
  • Autovacuum configuration settings

Security

  • All queries are wrapped in READ ONLY transactions by default
  • EXPLAIN ANALYZE is restricted to SELECT queries only
  • DDL/DML statements are rejected in ANALYZE mode
  • No data modification queries are allowed

Contributing

  1. Clone the repo
  2. npm install
  3. npm run build — TypeScript compilation
  4. npm test — Run unit tests (vitest)
  5. npm run dev — Watch mode for development

Limitations & Known Issues

  • Read-only: All queries use read-only connections. Cannot modify data or schema.
  • pg_stat_statements required: Slow query analysis on PostgreSQL requires the pg_stat_statements extension to be installed and loaded.
  • MySQL performance_schema: Index usage and scan statistics require performance_schema to be enabled (off by default in some MySQL installations).
  • SQLite: No index usage statistics available (SQLite doesn't track this). Sequential scan analysis and slow query detection are not supported for SQLite.
  • Large databases: Schema inspection on databases with 500+ tables may produce very long output. Use the schema parameter to limit scope.
  • Table name parameterization: SQLite PRAGMA statements use string interpolation for table names (SQLite does not support parameterized PRAGMAs). Table names are sourced from sqlite_master system table.
  • Cross-database queries: Cannot analyze queries that span multiple databases or use database links.
  • Estimated row counts: MySQL TABLE_ROWS in information_schema is an estimate, not exact.
  • Schema scope: All tools default to public schema. Non-public schemas require explicit specification. Multi-schema analysis requires running tools per schema separately.
  • Connection analysis: analyze_connections is PostgreSQL/MySQL only. Not available for SQLite databases.
  • Vacuum analysis: analyze_vacuum is PostgreSQL only. For MySQL, use OPTIMIZE TABLE or analyze_table_bloat.

Part of the MCP Java Backend Suite

License

MIT


End-of-life: 2026-05-10.

This MCP server is no longer maintained or distributed. The Corporation has pivoted to Apify marketplace actors. See irrationalways on Apify and irrcorp/bzp-poland-tenders for current Corporation work.

The npm package has been unpublished. The repository is archived for historical reference only.

Recommended Servers

playwright-mcp

playwright-mcp

A Model Context Protocol server that enables LLMs to interact with web pages through structured accessibility snapshots without requiring vision models or screenshots.

Official
Featured
TypeScript
Magic Component Platform (MCP)

Magic Component Platform (MCP)

An AI-powered tool that generates modern UI components from natural language descriptions, integrating with popular IDEs to streamline UI development workflow.

Official
Featured
Local
TypeScript
Audiense Insights MCP Server

Audiense Insights MCP Server

Enables interaction with Audiense Insights accounts via the Model Context Protocol, facilitating the extraction and analysis of marketing insights and audience data including demographics, behavior, and influencer engagement.

Official
Featured
Local
TypeScript
VeyraX MCP

VeyraX MCP

Single MCP tool to connect all your favorite tools: Gmail, Calendar and 40 more.

Official
Featured
Local
graphlit-mcp-server

graphlit-mcp-server

The Model Context Protocol (MCP) Server enables integration between MCP clients and the Graphlit service. Ingest anything from Slack to Gmail to podcast feeds, in addition to web crawling, into a Graphlit project - and then retrieve relevant contents from the MCP client.

Official
Featured
TypeScript
Kagi MCP Server

Kagi MCP Server

An MCP server that integrates Kagi search capabilities with Claude AI, enabling Claude to perform real-time web searches when answering questions that require up-to-date information.

Official
Featured
Python
E2B

E2B

Using MCP to run code via e2b.

Official
Featured
Neon Database

Neon Database

MCP server for interacting with Neon Management API and databases

Official
Featured
Exa Search

Exa Search

A Model Context Protocol (MCP) server lets AI assistants like Claude use the Exa AI Search API for web searches. This setup allows AI models to get real-time web information in a safe and controlled way.

Official
Featured
Qdrant Server

Qdrant Server

This repository is an example of how to create a MCP server for Qdrant, a vector search engine.

Official
Featured