pgtuner-mcp 0.4.0

An MCP server for PostgreSQL AI-powered performance tuning with HypoPG support

PostgreSQL Performance Tuning MCP

A Model Context Protocol (MCP) server that provides AI-powered PostgreSQL performance tuning capabilities. This server helps identify slow queries, recommend optimal indexes, analyze execution plans, and leverage HypoPG for hypothetical index testing.

Features

Query Analysis

• Retrieve slow queries from pg_stat_statements with detailed statistics
• Analyze query execution plans with EXPLAIN and EXPLAIN ANALYZE
• Identify performance bottlenecks with automated plan analysis
• Monitor active queries and detect long-running transactions

Index Tuning

• AI-powered index recommendations based on query workload analysis
• Hypothetical index testing with HypoPG extension (no disk usage)
• Find unused and duplicate indexes for cleanup
• Estimate index sizes before creation
• Test query plans with proposed indexes before implementing

Database Health

• Comprehensive health scoring with multiple checks
• Connection utilization monitoring
• Cache hit ratio analysis (buffer and index)
• Lock contention detection
• Vacuum health and transaction ID wraparound monitoring
• Replication lag monitoring
• Background writer and checkpoint analysis

Configuration Analysis

• Review PostgreSQL settings by category
• Get recommendations for memory, checkpoint, WAL, autovacuum, and connection settings
• Identify suboptimal configurations

MCP Prompts & Resources

• Pre-defined prompt templates for common tuning workflows
• Dynamic resources for table stats, index info, and health checks
• Comprehensive documentation resources

Installation

Standard Installation (for MCP clients like Claude Desktop)

pip install pgtuner_mcp

Or using uv:

uv pip install pgtuner_mcp

Manual Installation

git clone https://github.com/isdaniel/pgtuner_mcp.git
cd pgtuner_mcp
pip install -e .

Configuration

Environment Variables

Variable	Description	Required
DATABASE_URI	PostgreSQL connection string	Yes
PGTUNER_EXCLUDE_USERIDS	Comma-separated list of user IDs (OIDs) to exclude from monitoring	No

Connection String Format: postgresql://user:password@host:port/database

Excluding Specific Users from Monitoring

You can exclude specific PostgreSQL users from being included in query analysis and monitoring results. This is useful for filtering out:

• Monitoring or replication users
• System accounts
• Internal application service accounts

Set the PGTUNER_EXCLUDE_USERIDS environment variable with a comma-separated list of user OIDs:

# Exclude user IDs 16384, 16385, and 16386
export PGTUNER_EXCLUDE_USERIDS="16384,16385,16386"

To find the OID for a specific PostgreSQL user:

SELECT usesysid, usename FROM pg_user WHERE usename = 'monitoring_user';

When configured, the following queries are filtered:

• pg_stat_activity queries (filters on usesysid column)
• pg_stat_statements queries (filters on userid column)

This affects tools like get_slow_queries, get_active_queries, analyze_wait_events, check_database_health, and get_index_recommendations.

MCP Client Configuration

Add to your cline_mcp_settings.json or Claude Desktop config:

{
  "mcpServers": {
    "pgtuner_mcp": {
      "command": "python",
      "args": ["-m", "pgtuner_mcp"],
      "env": {
        "DATABASE_URI": "postgresql://user:password@localhost:5432/mydb"
      },
      "disabled": false,
      "autoApprove": []
    }
  }
}

Or Streamable HTTP Mode

{
  "mcpServers": {
    "pgtuner_mcp": {
      "type": "http",
      "url": "http://localhost:8080/mcp"
    }
  }
}

Server Modes

1. Standard MCP Mode (Default)

# Default mode (stdio)
python -m pgtuner_mcp

# Explicitly specify stdio mode
python -m pgtuner_mcp --mode stdio

2. HTTP SSE Mode (Legacy Web Applications)

The SSE (Server-Sent Events) mode provides a web-based transport for MCP communication. It's useful for web applications and clients that need HTTP-based communication.

# Start SSE server on default host/port (0.0.0.0:8080)
python -m pgtuner_mcp --mode sse

# Specify custom host and port
python -m pgtuner_mcp --mode sse --host localhost --port 3000

# Enable debug mode
python -m pgtuner_mcp --mode sse --debug

SSE Endpoints:

Endpoint	Method	Description
/sse	GET	SSE connection endpoint - clients connect here to receive server events
/messages	POST	Send messages/requests to the server

MCP Client Configuration for SSE:

For MCP clients that support SSE transport (like Claude Desktop or custom clients):

{
  "mcpServers": {
    "pgtuner_mcp": {
      "type": "sse",
      "url": "http://localhost:8080/sse"
    }
  }
}

3. Streamable HTTP Mode (Modern MCP Protocol - Recommended)

The streamable-http mode implements the modern MCP Streamable HTTP protocol with a single /mcp endpoint. It supports both stateful (session-based) and stateless modes.

# Start Streamable HTTP server in stateful mode (default)
python -m pgtuner_mcp --mode streamable-http

# Start in stateless mode (fresh transport per request)
python -m pgtuner_mcp --mode streamable-http --stateless

# Specify custom host and port
python -m pgtuner_mcp --mode streamable-http --host localhost --port 8080

# Enable debug mode
python -m pgtuner_mcp --mode streamable-http --debug

Stateful vs Stateless:

• Stateful (default): Maintains session state across requests using mcp-session-id header. Ideal for long-running interactions.
• Stateless: Creates a fresh transport for each request with no session tracking. Ideal for serverless deployments or simple request/response patterns.

Endpoint: http://{host}:{port}/mcp

Available Tools

Note: All tools focus exclusively on user/application tables and indexes. System catalog tables (pg_catalog, information_schema, pg_toast) are automatically excluded from all analyses.

Performance Analysis Tools

Tool	Description
get_slow_queries	Retrieve slow queries from pg_stat_statements with detailed stats (total time, mean time, calls, cache hit ratio). Excludes system catalog queries.
analyze_query	Analyze a query's execution plan with EXPLAIN ANALYZE, including automated issue detection
get_table_stats	Get detailed table statistics including size, row counts, dead tuples, and access patterns

Index Tuning Tools

Tool	Description
get_index_recommendations	AI-powered index recommendations based on query workload analysis
explain_with_indexes	Run EXPLAIN with hypothetical indexes to test improvements without creating real indexes
manage_hypothetical_indexes	Create, list, drop, or reset HypoPG hypothetical indexes. Supports hide/unhide existing indexes.
find_unused_indexes	Find unused and duplicate indexes that can be safely dropped

Database Health Tools

Tool	Description
check_database_health	Comprehensive health check with scoring (connections, cache, locks, replication, wraparound, disk, checkpoints)
get_active_queries	Monitor active queries, find long-running transactions and blocked queries. By default excludes system processes.
analyze_wait_events	Analyze wait events to identify I/O, lock, or CPU bottlenecks. Focuses on client backend processes.
review_settings	Review PostgreSQL settings by category with optimization recommendations

Bloat Detection Tools (pgstattuple)

Tool	Description
analyze_table_bloat	Analyze table bloat using pgstattuple extension. Shows dead tuple counts, free space, and wasted space percentage.
analyze_index_bloat	Analyze B-tree index bloat using pgstatindex. Shows leaf density, fragmentation, and empty/deleted pages. Also supports GIN and Hash indexes.
get_bloat_summary	Get a comprehensive overview of database bloat with top bloated tables/indexes, total reclaimable space, and priority maintenance actions.

Tool Parameters

get_slow_queries

• limit: Maximum queries to return (default: 10)
• min_calls: Minimum call count filter (default: 1)
• min_mean_time_ms: Minimum mean (average) execution time in milliseconds filter
• order_by: Sort by mean_time, calls, or rows

analyze_query

• query (required): SQL query to analyze
• analyze: Execute query with EXPLAIN ANALYZE (default: true)
• buffers: Include buffer statistics (default: true)
• format: Output format - json, text, yaml, xml

get_index_recommendations

• workload_queries: Optional list of specific queries to analyze
• max_recommendations: Maximum recommendations (default: 10)
• min_improvement_percent: Minimum improvement threshold (default: 10%)
• include_hypothetical_testing: Test with HypoPG (default: true)
• target_tables: Focus on specific tables

check_database_health

• include_recommendations: Include actionable recommendations (default: true)
• verbose: Include detailed statistics (default: false)

analyze_table_bloat

• table_name: Name of a specific table to analyze (optional)
• schema_name: Schema name (default: public)
• use_approx: Use pgstattuple_approx for faster analysis on large tables (default: false)
• min_table_size_gb: Minimum table size in GB to include in schema-wide scan (default: 5)
• include_toast: Include TOAST table analysis (default: false)

analyze_index_bloat

• index_name: Name of a specific index to analyze (optional)
• table_name: Analyze all indexes on this table (optional)
• schema_name: Schema name (default: public)
• min_index_size_gb: Minimum index size in GB to include (default: 5)
• min_bloat_percent: Only show indexes with bloat above this percentage (default: 20)

get_bloat_summary

• schema_name: Schema to analyze (default: public)
• top_n: Number of top bloated objects to show (default: 10)
• min_size_gb: Minimum object size in GB to include (default: 5)

MCP Prompts

The server includes pre-defined prompt templates for guided tuning sessions:

Prompt	Description
diagnose_slow_queries	Systematic slow query investigation workflow
index_optimization	Comprehensive index analysis and cleanup
health_check	Full database health assessment
query_tuning	Optimize a specific SQL query
performance_baseline	Generate a baseline report for comparison

MCP Resources

Static Resources

• pgtuner://docs/tools - Complete tool documentation
• pgtuner://docs/workflows - Common tuning workflows guide
• pgtuner://docs/prompts - Prompt template documentation

Dynamic Resource Templates

• pgtuner://table/{schema}/{table_name}/stats - Table statistics
• pgtuner://table/{schema}/{table_name}/indexes - Table index information
• pgtuner://query/{query_hash}/stats - Query performance statistics
• pgtuner://settings/{category} - PostgreSQL settings (memory, checkpoint, wal, autovacuum, connections, all)
• pgtuner://health/{check_type} - Health checks (connections, cache, locks, replication, bloat, all)

PostgreSQL Extension Setup

HypoPG Extension

HypoPG enables testing indexes without actually creating them. This is extremely useful for:

• Testing if a proposed index would be used by the query planner
• Comparing execution plans with different index strategies
• Estimating storage requirements before committing

Enable HypoPG in Database

HypoPG enables testing hypothetical indexes without creating them on disk.

-- Create the extension
CREATE EXTENSION IF NOT EXISTS hypopg;

-- Verify installation
SELECT * FROM hypopg_list_indexes();

pg_stat_statements Extension

The pg_stat_statements extension is required for query performance analysis. It tracks planning and execution statistics for all SQL statements executed by a server.

Step 1: Enable the Extension in postgresql.conf

Add the following to your postgresql.conf file:

# Required: Load pg_stat_statements module
shared_preload_libraries = 'pg_stat_statements'

# Required: Enable query identifier computation
compute_query_id = on

# Maximum number of statements tracked (default: 5000)
pg_stat_statements.max = 10000

# Track all statements including nested ones (default: top)
# Options: top, all, none
pg_stat_statements.track = top

# Track utility commands like CREATE, ALTER, DROP (default: on)
pg_stat_statements.track_utility = on

Note: After modifying shared_preload_libraries, a PostgreSQL server restart is required.

Step 2: Create the Extension in Your Database

-- Connect to your database and create the extension
CREATE EXTENSION IF NOT EXISTS pg_stat_statements;

-- Verify installation
SELECT * FROM pg_stat_statements LIMIT 1;

pgstattuple Extension

The pgstattuple extension is required for bloat detection tools (analyze_table_bloat, analyze_index_bloat, get_bloat_summary). It provides functions to get tuple-level statistics for tables and indexes.

-- Create the extension
CREATE EXTENSION IF NOT EXISTS pgstattuple;

-- Verify installation
SELECT * FROM pgstattuple('pg_class') LIMIT 1;

Performance Impact Considerations

Setting	Overhead	Recommendation
pg_stat_statements	Low (~1-2%)	Always enable
track_io_timing	Low-Medium (~2-5%)	Enable in production, test first
track_functions = all	Low	Enable for function-heavy workloads
pg_stat_statements.track_planning	Medium	Enable only when investigating planning issues
log_min_duration_statement	Low	Recommended for slow query identification

Tip: Use pg_test_timing to measure the timing overhead on your specific system before enabling track_io_timing.

Example Usage

Find and Analyze Slow Queries

# Get top 10 slowest queries
slow_queries = await get_slow_queries(limit=10, order_by="total_time")

# Analyze a specific query's execution plan
analysis = await analyze_query(
    query="SELECT * FROM orders WHERE user_id = 123",
    analyze=True,
    buffers=True
)

Get Index Recommendations

# Analyze workload and get recommendations
recommendations = await get_index_recommendations(
    max_recommendations=5,
    min_improvement_percent=20,
    include_hypothetical_testing=True
)

# Recommendations include CREATE INDEX statements
for rec in recommendations["recommendations"]:
    print(rec["create_statement"])

Database Health Check

# Run comprehensive health check
health = await check_database_health(
    include_recommendations=True,
    verbose=True
)

print(f"Health Score: {health['overall_score']}/100")
print(f"Status: {health['status']}")

# Review specific areas
for issue in health["issues"]:
    print(f"{issue}")

Find Unused Indexes

# Find indexes that can be dropped
unused = await find_unused_indexes(
    schema_name="public",
    include_duplicates=True
)

# Get DROP statements
for stmt in unused["recommendations"]:
    print(stmt)

Docker

docker pull  dog830228/pgtuner_mcp

# Streamable HTTP mode (recommended for web applications)
docker run -p 8080:8080 \
  -e DATABASE_URI=postgresql://user:pass@host:5432/db \
  dog830228/pgtuner_mcp --mode streamable-http

# Streamable HTTP stateless mode (for serverless)
docker run -p 8080:8080 \
  -e DATABASE_URI=postgresql://user:pass@host:5432/db \
  dog830228/pgtuner_mcp --mode streamable-http --stateless

# SSE mode (legacy web applications)
docker run -p 8080:8080 \
  -e DATABASE_URI=postgresql://user:pass@host:5432/db \
  dog830228/pgtuner_mcp --mode sse

# stdio mode (for MCP clients like Claude Desktop)
docker run -i \
  -e DATABASE_URI=postgresql://user:pass@host:5432/db \
  dog830228/pgtuner_mcp --mode stdio

Requirements

• Python: 3.10+
• PostgreSQL: 12+ (recommended: 14+)
• Extensions:
  • pg_stat_statements (required for query analysis)
  • hypopg (optional, for hypothetical index testing)

Dependencies

Core dependencies:

• mcp[cli]>=1.12.0 - Model Context Protocol SDK
• psycopg[binary,pool]>=3.1.0 - PostgreSQL adapter with connection pooling
• pglast>=7.10 - PostgreSQL query parser

Optional (for HTTP modes):

• starlette>=0.27.0 - ASGI framework
• uvicorn>=0.23.0 - ASGI server

Contributing

Contributions are welcome! Please feel free to submit a Pull Request.