Overview

PostgreSQL MCP is a Model Context Protocol server that provides AI assistants with direct access to PostgreSQL databases. The reference implementation, maintained by Anthropic as part of the official MCP servers collection, offers read-only query execution and schema inspection capabilities through a standardized interface.

PostgreSQL is the backbone of countless production systems, from startup MVPs to enterprise data warehouses. The MCP server enables AI assistants to inspect database schemas, discover table structures, and execute SELECT queries — turning conversational AI into a powerful data exploration and analysis tool. Teams can ask natural language questions about their data and receive SQL-backed answers without writing queries manually.

Multiple implementations exist beyond the reference server, including full read-write variants and performance analysis tools like Postgres MCP Pro. However, the governance challenge remains consistent: database access through AI introduces risks around data exposure, query performance, and — in write-enabled configurations — data integrity. For organizations with sensitive customer data, financial records, or regulated information in PostgreSQL, governing this connector is non-negotiable.

Key Features

Schema Discovery

Automatically inspect database metadata to discover schemas, tables, columns, data types, and relationships, enabling accurate query generation without manual documentation

Read-Only Query Execution

Execute SELECT queries against PostgreSQL databases through natural language requests with read-only access enforced by default to prevent accidental data modification

Table & Column Inspection

Retrieve detailed schema information for individual tables including column names, data types, constraints, indexes, and foreign key relationships

Multi-Schema Support

Navigate across multiple database schemas within a single connection, useful for organizations that segment data by tenant, environment, or domain

Connection String Configuration

Configure database access through standard PostgreSQL connection strings supporting SSL, connection pooling, and authentication parameters for read replicas or restricted roles

Capabilities

PostgreSQL MCP exposes 5 tools for AI agents. 1 require approval.

4 Read1 Write

Tool	Operation	Risk	Description
`query` Executes a SELECT query	Read	Medium Risk	Executes a SELECT query
`execute`Approval Executes INSERT/UPDATE/DELETE	Write	High Risk	Executes INSERT/UPDATE/DELETE
`describe_table` Shows table schema	Read	Low Risk	Shows table schema
`list_tables` Lists all tables	Read	Low Risk	Lists all tables
`list_schemas` Lists database schemas	Read	Low Risk	Lists database schemas

Use Cases

Strategy-Aligned Use Cases

Ad-Hoc Data Analysis

Business teams can ask natural language questions about data stored in PostgreSQL and receive SQL-backed answers. Reduces dependency on data engineering teams for routine analytical queries and accelerates decision-making.

Schema Understanding & Documentation

AI assistants can explore database structures and generate documentation for tables, relationships, and data flows. Particularly valuable for onboarding engineers to unfamiliar databases or maintaining up-to-date data dictionaries.

Report Generation & KPI Tracking

Automate recurring reports by having AI assistants query PostgreSQL for key metrics, format results, and distribute summaries. Connect database insights to strategic objectives tracked in tools like Linear or Notion.

Data Quality Monitoring

AI assistants can run validation queries to check for null values, orphaned records, constraint violations, and data anomalies. Surface data quality issues proactively rather than discovering them in production.

Integrations

Used By

Data Plugin

Database queries

Finance Plugin

Financial analytics

Considerations

Before You Adopt

**Sensitive Data Exposure**: PostgreSQL databases frequently contain PII, financial records, and regulated information — AI queries returning sensitive columns can violate GDPR, HIPAA, or SOC 2 requirements
**Write-Enabled Variants**: Several community implementations offer full INSERT, UPDATE, and DELETE capabilities that must be gated behind approval workflows with proper transaction management
**Query Performance Impact**: AI-generated queries can create expensive full table scans, cartesian joins, or recursive queries that impact production database performance without read replica routing
**Connection Credential Management**: Database connection strings contain host addresses and credentials that must be managed through secure secret stores rather than embedded in MCP configuration files
**Data Exfiltration Risk**: An AI assistant with database read access could extract large volumes of data through sequential queries, requiring rate limiting, result set size caps, and query pattern monitoring

Stratafy Fit

Integration Potential

5/5

PostgreSQL MCP is a critical governance target for Stratafy. Databases are the most sensitive data layer in any organization, and AI access to production data demands rigorous controls. Stratafy can enforce read-only access as the default policy, restrict which schemas and tables are queryable by role, implement query result size limits to prevent bulk data extraction, route AI queries through read replicas to protect production performance, and maintain detailed audit logs of every query executed through AI. For organizations in regulated industries, Stratafy provides the compliance evidence needed to demonstrate controlled AI access to sensitive data stores.