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MCP PostgreSQL Server

Anthropic (Archived)

79·Strong

Overall Trust Score

ARCHIVED: Former official MCP server for PostgreSQL database interaction. This server is NO LONGER MAINTAINED and has been moved to servers-archived repository. No security updates provided. Community alternatives available. Use with caution.

database
sql
mcp
model-context-protocol
Version: 0.6.2
Last Evaluated: November 9, 2025
Official Website →

Trust Vector

Performance & Reliability

87
query execution accuracy
88
Methodology
Query execution testing
Evidence
PostgreSQL Driver Integration
Uses standard PostgreSQL drivers with high query accuracy
Date: 2025-11-16
Confidence: highLast verified: 2025-11-09
schema introspection
92
Methodology
Schema discovery testing
Evidence
MCP Postgres Implementation
Comprehensive schema inspection including tables, columns, indexes, and constraints
Date: 2025-11-16
Confidence: highLast verified: 2025-11-09
connection stability
85
Methodology
Connection reliability testing
Evidence
PostgreSQL Reliability
Built on PostgreSQL's robust connection pooling and stability
Date: 2025-11-16
Confidence: highLast verified: 2025-11-09
large result handling
82
Methodology
Large dataset performance testing
Evidence
MCP Server Implementation
Handles large result sets with pagination and limits
Date: 2025-11-16
Confidence: mediumLast verified: 2025-11-09
error handling
86
Methodology
Error scenario testing
Evidence
PostgreSQL Error Reporting
Detailed error messages from PostgreSQL with proper propagation
Date: 2025-11-16
Confidence: highLast verified: 2025-11-09

Security

71
sql injection protection
82
Methodology
SQL injection testing
Evidence
Parameterized Queries
Uses parameterized queries to prevent SQL injection
Date: 2025-11-16
Confidence: highLast verified: 2025-11-09
access control
75
Methodology
Permission boundary testing
Evidence
PostgreSQL Permissions
Inherits database user permissions but AI can execute any query within those permissions
Date: 2025-11-16
Confidence: mediumLast verified: 2025-11-09
data modification risk
65
Methodology
Write operation risk assessment
Evidence
Security Analysis
AI can execute INSERT, UPDATE, DELETE operations if database user has permissions
Date: 2025-11-16
Confidence: highLast verified: 2025-11-09
credential security
72
Methodology
Credential storage review
Evidence
Connection String Management
Credentials stored in configuration; requires secure credential management
Date: 2025-11-16
Confidence: mediumLast verified: 2025-11-09
audit logging
68
Methodology
Logging capabilities assessment
Evidence
PostgreSQL Logging
Depends on PostgreSQL audit configuration; MCP server provides basic query logging
Date: 2025-11-16
Confidence: mediumLast verified: 2025-11-09

Privacy & Compliance

68
data exposure risk
62
Methodology
Data flow and exposure analysis
Evidence
Data Flow Analysis
Query results including sensitive data sent to LLM provider
Date: 2025-11-16
Confidence: highLast verified: 2025-11-09
pii protection
60
Methodology
Privacy controls assessment
Evidence
MCP Security Guidelines
No built-in PII detection, redaction, or anonymization
Date: 2025-11-16
Confidence: highLast verified: 2025-11-09
query result filtering
70
Methodology
Data filtering capabilities review
Evidence
MCP Server Configuration
Can limit result rows but no column-level filtering for sensitive data
Date: 2025-11-16
Confidence: mediumLast verified: 2025-11-09
compliance readiness
65
Methodology
Compliance framework review
Evidence
Compliance Assessment
GDPR/HIPAA compliance depends on LLM provider and data handling configuration
Date: 2025-11-16
Confidence: mediumLast verified: 2025-11-09
schema exposure
75
Methodology
Metadata exposure analysis
Evidence
Schema Introspection
Database schema metadata shared with LLM for query generation
Date: 2025-11-16
Confidence: highLast verified: 2025-11-09

Trust & Transparency

84
documentation quality
88
Methodology
Documentation completeness review
Evidence
MCP Postgres Docs
Clear documentation with setup instructions and security considerations
Date: 2025-11-16
Confidence: highLast verified: 2025-11-09
query visibility
90
Methodology
Query traceability assessment
Evidence
MCP Protocol Logging
All SQL queries logged and visible in MCP message stream
Date: 2025-11-16
Confidence: highLast verified: 2025-11-09
open source code
95
Methodology
Source code review
Evidence
GitHub Repository
Fully open source with MIT license
Date: 2025-11-16
Confidence: highLast verified: 2025-11-09
security documentation
75
Methodology
Security documentation review
Evidence
Security Guidelines
Provides security best practices but could be more comprehensive for database access
Date: 2025-11-16
Confidence: mediumLast verified: 2025-11-09

Operational Excellence

83
ease of setup
85
Methodology
Setup complexity assessment
Evidence
MCP Quickstart
Straightforward setup requiring connection string configuration
Date: 2025-11-16
Confidence: highLast verified: 2025-11-09
performance
84
Methodology
Performance benchmarking
Evidence
PostgreSQL Performance
Query performance depends on database configuration and optimization
Date: 2025-11-16
Confidence: highLast verified: 2025-11-09
connection pooling
82
Methodology
Connection management review
Evidence
Implementation Details
Supports connection pooling for efficient resource utilization
Date: 2025-11-16
Confidence: mediumLast verified: 2025-11-09
error diagnostics
80
Methodology
Error messaging assessment
Evidence
PostgreSQL Error Messages
Detailed error messages help diagnose issues
Date: 2025-11-16
Confidence: mediumLast verified: 2025-11-09
community support
80
Methodology
Community activity analysis
Evidence
MCP Community
Growing community support for database MCP servers
Date: 2025-11-16
Confidence: mediumLast verified: 2025-11-09

✨ Strengths

  • •Comprehensive PostgreSQL feature support including schema introspection
  • •Natural language to SQL query generation capabilities
  • •Excellent for data analysis and business intelligence workflows
  • •Full query visibility and logging for audit purposes
  • •Open source implementation with active development
  • •Supports connection pooling and performance optimization

⚠️ Limitations

  • •High risk of exposing sensitive database content to LLM providers
  • •No built-in PII detection, redaction, or data anonymization
  • •AI can execute destructive operations (DELETE, DROP) if permissions allow
  • •Limited granular access control beyond database user permissions
  • •Query results with sensitive data sent to external APIs
  • •Compliance challenges for regulated industries (HIPAA, PCI-DSS, GDPR)

📊 Metadata

license: MIT
supported platforms:
0: All platforms with PostgreSQL client support
programming languages:
0: TypeScript
1: Python
mcp version: 1.0
github repo: https://github.com/modelcontextprotocol/servers-archived
github stars: 58700
database version: PostgreSQL 10+
connection method: Connection string with credentials
first release: 2024-11
maintained by: None (Archived)
status: Archived - No longer maintained
transport types:
0: stdio
installation methods:
0: npm

Use Case Ratings

code generation

78

Useful for generating database migration scripts and query code

customer support

75

Can help support teams query customer data, but privacy concerns exist

content creation

65

Limited applicability; mainly for content stored in databases

data analysis

92

Excellent for AI-powered data analysis, reporting, and insights generation

research assistant

85

Good for analyzing research datasets stored in PostgreSQL

legal compliance

55

High risk due to potential exposure of sensitive legal data to LLM providers

healthcare

50

Not recommended for PHI due to data exposure risks; HIPAA compliance challenges

financial analysis

58

Risky for sensitive financial data; requires strict access controls and data filtering

education

82

Good for analyzing student data and learning analytics

creative writing

60

Limited utility unless creative content is database-driven