The PostgreSQL MCP Server assists with PostgreSQL database management through three main functions:
Database Analysis: Analyze database configuration, performance, and security with optimization recommendations.
Setup Instructions: Provide platform-specific installation steps for Linux, macOS, and Windows, along with configuration and security best practices.
Database Debugging: Troubleshoot common issues like connection problems, performance bottlenecks, lock conflicts, and replication status.
Offers platform-specific PostgreSQL installation and configuration guidance for Linux systems
Offers platform-specific PostgreSQL installation and configuration guidance for macOS systems
Provides PostgreSQL database management capabilities including analysis, setup instructions, and debugging for PostgreSQL database instances
Click on "Install Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type @ followed by the MCP server name and your instructions, e.g., "@PostgreSQL MCP Server analyze performance for my production database"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
A Model Context Protocol (MCP) server that provides PostgreSQL database management capabilities. This server assists with analyzing existing PostgreSQL setups, providing implementation guidance, and debugging database issues.
analyze_database)Analyzes PostgreSQL database configuration and performance metrics:
Configuration analysis
Performance metrics
Security assessment
Recommendations for optimization
// Example usage
{
"connectionString": "postgresql://user:password@localhost:5432/dbname",
"analysisType": "performance" // Optional: "configuration" | "performance" | "security"
}get_setup_instructions)Provides step-by-step PostgreSQL installation and configuration guidance:
Platform-specific installation steps
Configuration recommendations
Security best practices
Post-installation tasks
// Example usage
{
"platform": "linux", // Required: "linux" | "macos" | "windows"
"version": "15", // Optional: PostgreSQL version
"useCase": "production" // Optional: "development" | "production"
}debug_database)Debug common PostgreSQL issues:
Connection problems
Performance bottlenecks
Lock conflicts
Replication status
// Example usage
{
"connectionString": "postgresql://user:password@localhost:5432/dbname",
"issue": "performance", // Required: "connection" | "performance" | "locks" | "replication"
"logLevel": "debug" // Optional: "info" | "debug" | "trace"
}Related MCP server: Postgres MCP Pro
Node.js >= 18.0.0
PostgreSQL server (for target database operations)
Network access to target PostgreSQL instances
To install PostgreSQL MCP Server for Claude Desktop automatically via Smithery:
npx -y @smithery/cli install @nahmanmate/postgresql-mcp-server --client claudeClone the repository
Install dependencies:
npm installBuild the server:
npm run buildAdd to MCP settings file:
{
"mcpServers": {
"postgresql-mcp": {
"command": "node",
"args": ["/path/to/postgresql-mcp-server/build/index.js"],
"disabled": false,
"alwaysAllow": []
}
}
}npm run dev - Start development server with hot reload
npm run lint - Run ESLint
npm test - Run tests
Connection Security
Uses connection pooling
Implements connection timeouts
Validates connection strings
Supports SSL/TLS connections
Query Safety
Validates SQL queries
Prevents dangerous operations
Implements query timeouts
Logs all operations
Authentication
Supports multiple authentication methods
Implements role-based access control
Enforces password policies
Manages connection credentials securely
Always use secure connection strings with proper credentials
Follow production security recommendations for sensitive environments
Regularly monitor and analyze database performance
Keep PostgreSQL version up to date
Implement proper backup strategies
Use connection pooling for better resource management
Implement proper error handling and logging
Regular security audits and updates
The server implements comprehensive error handling:
Connection failures
Query timeouts
Authentication errors
Permission issues
Resource constraints
The evals package loads an mcp client that then runs the index.ts file, so there is no need to rebuild between tests. You can see the full documentation here.
OPENAI_API_KEY=your-key npx mcp-eval src/evals/evals.ts src/index.tsFork the repository
Create a feature branch
Commit your changes
Push to the branch
Create a Pull Request
This project is licensed under the AGPLv3 License - see LICENSE file for details.
Analyze PostgreSQL database configuration and performance
| Name | Required | Description | Default |
|---|---|---|---|
| connectionString | Yes | PostgreSQL connection string | |
| analysisType | No | Type of analysis to perform |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure but only states what the tool does without detailing traits like whether it's read-only, requires specific permissions, has rate limits, or what the output format might be. This leaves significant gaps in understanding the tool's behavior.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence that directly states the tool's purpose without any unnecessary words or fluff. It is appropriately sized and front-loaded, making it easy to parse quickly.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the complexity of database analysis, lack of annotations, and absence of an output schema, the description is insufficient. It doesn't explain what the analysis entails, what results to expect, or any behavioral traits, leaving the agent with incomplete context for effective tool use.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema description coverage is 100%, meaning the input schema already documents both parameters ('connectionString' and 'analysisType') with descriptions and an enum. The description adds no additional meaning beyond what the schema provides, so it meets the baseline score of 3 for adequate but unenhanced parameter information.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose with a specific verb ('analyze') and resource ('PostgreSQL database configuration and performance'), making it easy to understand what the tool does. However, it doesn't explicitly differentiate from sibling tools like 'debug_database' or 'get_setup_instructions', which prevents a perfect score.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives like 'debug_database' or 'get_setup_instructions'. It lacks any context about prerequisites, such as needing a valid connection string, or exclusions, leaving the agent without clear usage instructions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Debug common PostgreSQL issues
| Name | Required | Description | Default |
|---|---|---|---|
| connectionString | Yes | PostgreSQL connection string | |
| issue | Yes | Type of issue to debug | |
| logLevel | No | Logging detail level | info |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries full burden but only states 'Debug common PostgreSQL issues', lacking details on behavior such as what the tool does (e.g., runs diagnostics, generates reports, modifies settings), permissions required, side effects, or output format. It doesn't disclose if it's read-only, destructive, or has rate limits, which is a significant gap for a debugging tool.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence with zero waste, front-loaded and appropriately sized for its purpose. It avoids redundancy and is structured to convey the core idea without unnecessary elaboration.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the complexity of debugging (potentially involving diagnostics, analysis, or fixes), no annotations, and no output schema, the description is incomplete. It doesn't explain what the tool returns, how it handles different issue types, or behavioral traits, leaving gaps that could hinder correct agent invocation.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the schema fully documents parameters like 'connectionString', 'issue' with enums, and 'logLevel'. The description adds no meaning beyond this, as it doesn't explain parameter interactions or provide examples. Baseline 3 is appropriate since the schema handles the heavy lifting.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description 'Debug common PostgreSQL issues' states a general purpose but lacks specificity about what debugging entails (e.g., diagnostics, fixes, logs) and doesn't clearly distinguish from sibling tools like 'analyze_database' or 'get_setup_instructions'. It's vague about the verb 'debug'—whether it analyzes, reports, or resolves issues.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance is provided on when to use this tool versus alternatives like 'analyze_database' or 'get_setup_instructions'. The description implies usage for PostgreSQL issues but doesn't specify contexts, prerequisites, or exclusions, leaving the agent to infer based on tool names alone.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Get step-by-step PostgreSQL setup instructions
| Name | Required | Description | Default |
|---|---|---|---|
| version | No | PostgreSQL version to install | |
| platform | Yes | Operating system platform | |
| useCase | No | Intended use case |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It states the tool provides 'step-by-step instructions,' implying a read-only, informational output, but doesn't clarify aspects like response format, potential side effects, or error handling, which are important for a tool with parameters.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence that front-loads the core purpose ('Get step-by-step PostgreSQL setup instructions') with zero wasted words, making it highly concise and well-structured.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's moderate complexity (3 parameters, no annotations, no output schema), the description is minimally adequate. It covers the purpose but lacks details on behavior, usage context, or output, leaving gaps that could hinder effective tool selection and invocation.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the schema already documents all parameters (version, platform, useCase) with descriptions and enums. The description adds no additional parameter details beyond implying setup instructions, which aligns with the schema but doesn't enhance it, meeting the baseline for high coverage.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the action ('Get step-by-step... instructions') and resource ('PostgreSQL setup'), making the purpose understandable. However, it doesn't differentiate from sibling tools like 'analyze_database' or 'debug_database', which likely serve different purposes but aren't contrasted here.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance is provided on when to use this tool versus alternatives. The description lacks context on prerequisites, timing, or comparisons to sibling tools, leaving the agent without usage direction beyond the basic purpose.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Recent tool additions, removals, and schema changes observed during successful MCP inspections. Dates show when Glama detected each change.
analyze_databasedebug_databaseget_setup_instructionsEach tool has a clearly distinct purpose: analyze_database focuses on configuration and performance analysis, debug_database targets issue troubleshooting, and get_setup_instructions provides installation guidance. There is no overlap in functionality, making it easy for an agent to select the appropriate tool without confusion.
All tool names follow a consistent verb_noun pattern (analyze_database, debug_database, get_setup_instructions), using snake_case throughout. The naming is predictable and readable, with no deviations or mixed conventions.
With only 3 tools, the server feels thin for a PostgreSQL domain, which typically involves operations like querying, inserting, updating, or managing tables. While the tools cover analysis, debugging, and setup, the lack of core database interaction tools suggests an incomplete surface for typical agent workflows.
The tool set is severely incomplete for a PostgreSQL server, as it lacks basic CRUD operations (e.g., execute_query, create_table, insert_data) and management functions (e.g., list_tables, backup_database). This will cause significant agent failures when attempting to interact with the database beyond setup and diagnostics.
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