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PostgreSQL Database Tools: 38 Tools Organized Into 6 Use-Case Bundles

· 9 min read
Tony Lewis
MCPBundles

We just integrated PostgreSQL—the powerful open-source relational database—into MCPBundles. But here's the challenge: PostgreSQL exposes 38 different database tools covering everything from SQL queries to schema inspection to performance optimization. How do you make 38 tools discoverable and useful without overwhelming users?

The answer: use-case driven bundles. Instead of dumping 38 tools into one massive bundle, we organized them into 6 focused bundles based on what database professionals actually do. Every tool appears in the main "PostgreSQL" bundle, plus at least one specialized bundle aligned to specific workflows.

The Bundle Architecture​

Here's how we organized all 38 PostgreSQL tools into 6 focused bundles:

PostgreSQL (Main Bundle)​

All 38 tools — The complete toolkit for users who want everything

Use Cases → Bundles​

"I need to browse and search my database"​

Use Case: Data exploration and discovery

Bundle: Data Exploration (10 tools)

When you're exploring a database, you need to find records, sample data, compare tables, and understand what's stored. This bundle gives you:

  • Quick searches: Execute SQL queries or use structured syntax to find records
  • Table browsing: List and browse rows with rich filtering and pagination
  • Text search: Full-text search across text columns in multiple tables
  • Data sampling: Efficient random sampling using TABLESAMPLE for large tables
  • Table comparison: Compare two tables to find differences and duplicates
  • Data export: Export table data to CSV or JSON format

Example workflow: List tables → Browse users table → Search for specific records → Compare tables → Export results

"I need to understand my database structure"​

Use Case: Schema discovery and documentation

Bundle: Schema Discovery (10 tools)

Understanding database structure is essential before writing queries or making changes. This bundle helps you:

  • Table discovery: List all tables with metadata, columns, and relationships
  • Schema inspection: Deep-dive into table schemas with columns, types, and constraints
  • Relationship mapping: Visualize foreign key relationships as a graph
  • Dependency tracking: See what views, triggers, and functions depend on each table
  • Function discovery: List stored procedures and functions with definitions
  • Trigger analysis: Understand database automation and triggers
  • Unused table detection: Find orphaned tables that may be safe to remove

Example workflow: List all tables → Inspect schema → Map relationships → Check dependencies → Understand structure

"My queries are slow, I need to optimize performance"​

Use Case: Performance analysis and optimization

Bundle: Performance Analysis (9 tools)

Slow queries kill productivity. This bundle helps you diagnose and fix performance issues:

  • Query analysis: Run EXPLAIN ANALYZE to understand query execution plans
  • Slow query detection: Find slow queries from pg_stat_statements
  • Index analysis: Analyze index usage patterns and find unused indexes
  • Missing indexes: Get suggestions for indexes that would improve performance
  • Table statistics: Monitor table size, bloat, dead tuples, and storage stats
  • Lock detection: Show current locks and blocking queries
  • Maintenance suggestions: Get VACUUM and ANALYZE recommendations

Example workflow: Find slow queries → Explain query plan → Analyze indexes → Check table stats → Apply optimizations

"I need to validate data quality and find issues"​

Use Case: Data quality and profiling

Bundle: Data Quality & Profiling (9 tools)

Data quality issues cause bugs and bad decisions. This bundle helps you find and fix them:

  • Column profiling: Get statistical profiles (min, max, avg, median, nulls, distinct values)
  • Duplicate detection: Find duplicate rows based on specified columns
  • Constraint validation: Check for NOT NULL, UNIQUE, and CHECK constraint violations
  • NULL analysis: Analyze NULL value distribution across columns
  • Referential integrity: Find orphaned foreign key references
  • Outlier detection: Detect statistical outliers using IQR or z-score methods
  • Quality reports: Comprehensive data quality reports for entire tables

Example workflow: Profile columns → Find duplicates → Validate constraints → Check referential integrity → Fix issues

"I need to manage schema and execute SQL safely"​

Use Case: Development and operations

Bundle: Development & Operations (10 tools)

Building and maintaining databases requires safe, controlled operations. This bundle provides:

  • SQL execution: Execute any SQL statement (SELECT, INSERT, UPDATE, DELETE, DDL)
  • Table creation: Create tables from schema definitions
  • Schema alterations: Safe ALTER TABLE operations (add/drop columns, rename, etc.)
  • Backup/restore: Create table backups before making changes
  • Batch operations: Execute multiple statements in transactions (all-or-nothing)
  • Preview operations: Preview what would be affected by DELETE/UPDATE before executing
  • Dependency checking: Verify dependencies before dropping or altering tables

Example workflow: Create table → Backup existing table → Alter schema → Preview changes → Execute batch operations

The Design Philosophy: Every Tool in Context​

The key insight here is that database professionals don't think in terms of SQL functions—they think in terms of workflows:

  • "I need to find records in my database" → Data Exploration bundle
  • "I want to understand the schema structure" → Schema Discovery bundle
  • "My queries are slow" → Performance Analysis bundle
  • "I need to check data quality" → Data Quality & Profiling bundle
  • "I need to modify the schema" → Development & Operations bundle

By organizing tools into use-case bundles, we make the right tools discoverable at the moment you need them. This approach follows our bundle design philosophy where we organize tools around workflows, not API endpoints.

Cross-Bundle Tool Placement​

Some tools appear in multiple bundles because they serve different workflows:

postgres_get_ai_description appears in:

  • Data Exploration (get schema context for queries)
  • Schema Discovery (compact schema overview)
  • Performance Analysis (understand schema for optimization)
  • Data Quality & Profiling (schema context for validation)
  • Development & Operations (schema reference for changes)

postgres_list_tables appears in:

  • Data Exploration (discover available tables)
  • Schema Discovery (understand database structure)

postgres_get_table_dependencies appears in:

  • Schema Discovery (understand relationships)
  • Development & Operations (check dependencies before changes)

postgres_inspect_schema appears in:

  • Schema Discovery (detailed schema inspection)
  • Development & Operations (schema reference for operations)

postgres_get_enum_values appears in:

  • Data Exploration (understand categorical data)
  • Schema Discovery (detect enum patterns)
  • Data Quality & Profiling (validate enum-like columns)

This isn't duplication—it's contextual access. The same tool serves different purposes depending on whether you're exploring data, understanding schema, or making changes.

How This Improves AI Agent Usage​

When an AI agent (like Claude or ChatGPT) connects to PostgreSQL through MCPBundles, it matches your use case to the right bundle:

"Show me all users from California" → Loads Data Exploration bundle (10 tools) → Uses postgres_list_tables to discover tables → Uses postgres_list_rows to filter and browse data → Returns results efficiently

"Why is this query slow?" → Loads Performance Analysis bundle (9 tools) → Uses postgres_explain_query to analyze execution plan → Uses postgres_analyze_indexes to check index usage → Suggests optimizations

"Find duplicate email addresses in the users table" → Loads Data Quality & Profiling bundle (9 tools) → Uses postgres_find_duplicates to detect duplicates → Uses postgres_profile_column to understand data distribution → Provides cleanup recommendations

"I need to add a column to the users table" → Loads Development & Operations bundle (10 tools) → Uses postgres_get_table_dependencies to check dependencies → Uses postgres_backup_table to create backup → Uses postgres_alter_table to add column safely

Instead of scanning 38 tools, the AI starts with the right bundle based on your intent. This is exactly how tool parameter design should work—tools organized around intent, not technical structure.

Bundle Status and Credentials​

Each bundle shows its credential status:

  • Ready (green) — Credentials verified, bundle ready to use
  • Disabled (gray) — Bundle not activated yet
  • Error (red) — Credential issue needs attention

PostgreSQL uses connection string authentication (host, port, database, user, password), so once you connect your database, all 6 bundles use the same credential. But you can enable/disable bundles individually based on what you need.

Critical design rule: every tool appears in at least one sub-bundle. No orphaned tools. No hidden functionality.

  • 38 unique tools
  • 86 bundle links (38 main + 48 sub-bundle links)
  • 100% coverage — every tool is discoverable

This means whether you browse bundles by use case OR search tools by name, you'll find what you need. Every tool is organized into bundles that match how database professionals actually work—not how SQL APIs are structured.

Frequently Asked Questions​

Q: Do I need to enable all 6 bundles?

No. Enable only the bundles you need. If you're primarily exploring data, enable Data Exploration. If you're optimizing performance, enable Performance Analysis. You can always enable more bundles later as your needs change.

Q: Can I use multiple bundles at the same time?

Absolutely! Bundles are independent. You can have Data Exploration, Performance Analysis, and Data Quality & Profiling all enabled simultaneously. Each bundle uses the same PostgreSQL credentials.

Q: What if I need a tool that's not in any bundle?

Every tool appears in at least one sub-bundle. If you enable the main PostgreSQL bundle, you get all 38 tools. The sub-bundles are just for focused workflows.

Q: How do I know which bundle to use?

Think about your immediate goal:

  • Exploring data → Data Exploration
  • Understanding schema → Schema Discovery
  • Optimizing performance → Performance Analysis
  • Validating data → Data Quality & Profiling
  • Making schema changes → Development & Operations

Q: Can I disable bundles I'm not using?

Yes. You can enable/disable bundles individually. This helps keep your AI agent focused on the tools you actually need, reducing context window usage and improving response times.

Q: Does PostgreSQL support local connections?

Yes! PostgreSQL supports connecting via local proxy for databases running on your machine. Just check the "Connect via Local Proxy" option when setting up credentials.

What This Means for Database Automation​

With PostgreSQL + MCPBundles, you can now build AI-powered database workflows:

"Analyze my database performance" → Loads Performance Analysis bundle → Finds slow queries → Analyzes index usage → Suggests optimizations

"Find all data quality issues in my users table" → Loads Data Quality & Profiling bundle → Profiles columns → Finds duplicates → Validates constraints → Generates quality report

"Show me the database schema and relationships" → Loads Schema Discovery bundle → Lists all tables → Maps foreign key relationships → Shows dependencies → Provides schema overview

"Backup and modify the orders table" → Loads Development & Operations bundle → Creates backup → Shows what would be affected → Executes changes safely

All of this happens through natural language conversation with an AI agent. No manual SQL queries. No complex scripting. Just bundles that map to real database work.

Try It Yourself​

PostgreSQL is now available on MCPBundles with all 6 bundles ready to use:

  1. Connect your PostgreSQL database (host, port, database, user, password)
  2. Enable the bundles you need (or just enable "PostgreSQL" for everything)
  3. Start asking database questions in natural language

The AI will automatically route to the right bundle and use the right tools based on your intent.

Want to see this in action? Try the Data Exploration bundle free and start querying your database with AI-powered tools.

The future of database work isn't learning 38 different SQL functions—it's having AI agents that understand your workflows and use the right tools automatically. That's what use-case bundles enable.