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MCP Servers for Cursor AI: Centralized Control & Seamless Scaling - MCP Implementation

MCP Servers for Cursor AI: Centralized Control & Seamless Scaling

Manage, monitor, and scale your MCP servers effortlessly—all in one centralized powerhouse for Cursor AI. Streamlined control, optimized performance, seamless integration." )

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About MCP Servers for Cursor AI

What is MCP Servers for Cursor AI: Centralized Control & Seamless Scaling?

MCP Servers act as middleware protocols enabling Cursor AI to interact with external systems like databases and note-taking tools. These servers provide centralized control over configurations and resources while allowing seamless scaling to handle evolving workloads. The two core implementations—PostgreSQL and Obsidian MCP Servers—bridge AI capabilities with structured data management and knowledge base integration.

How to Use MCP Servers for Cursor AI: Centralized Control & Seamless Scaling?

Integration follows a three-step workflow:
1. Setup: Install required dependencies and configure environment variables for database credentials/API keys
2. Configuration: Define server paths and connection details in Cursor AI’s MCP config file (~/.cursor/mcp.json)
3. Execution: Run servers locally to expose API endpoints Cursor AI can interact with via natural language commands

MCP Servers for Cursor AI Features

Key Features of MCP Servers for Cursor AI: Centralized Control & Seamless Scaling?

  • PostgreSQL Server:
    • Real-time performance monitoring with query latency alerts
    • Schema migration and table management tools
    • Multi-database configuration support via PG_DB_MAP
  • Obsidian Server:
    • Advanced vault search with pattern matching
    • Content editing through patch/append operations
    • Batch file access for high-volume note processing

Use Cases of MCP Servers for Cursor AI: Centralized Control & Seamless Scaling?

  • Data teams optimizing PostgreSQL clusters with AI-driven performance tuning
  • Researchers querying Obsidian knowledge bases to generate literature reviews
  • Developers automating database migrations during CI/CD pipelines
  • Content managers maintaining consistent note hierarchies across projects

MCP Servers for Cursor AI FAQ

FAQ from MCP Servers for Cursor AI: Centralized Control & Seamless Scaling?

  • How do I troubleshoot installation issues? Use the mcp-inspector tool to validate server endpoints and dependency versions
  • Can I run multiple database instances? Yes, configure PG_DB_MAP with named connections for environment-specific setups
  • Where are logs stored? PostgreSQL logs at ~/Library/Logs/Cursor/mcp-server-postgresql-mcp.log
  • Is network exposure required? No, all operations execute locally to maintain data sovereignty
  • What if my Obsidian API key is compromised? Regenerate the API key from Obsidian’s plugin settings immediately

Content

MCP Servers for Cursor AI

This repository contains Model Context Protocol (MCP) server setups for Cursor AI, enabling integration with external tools and databases.

Available MCP Servers

1. PostgreSQL MCP Server

A full-featured Model Context Protocol (MCP) server that provides PostgreSQL database management capabilities. This server assists with analyzing existing PostgreSQL setups, providing implementation guidance, debugging database issues, managing schemas, migrating data, and monitoring database performance.

  • Location : ./postgresql-mcp/
  • Features : Database analysis, schema management, data migration, monitoring
  • Source : HenkDz/postgresql-mcp-server

Main Tools

  • analyze_database: Analyze PostgreSQL configuration and performance
  • get_setup_instructions: Get step-by-step PostgreSQL setup guidance
  • debug_database: Debug common PostgreSQL issues
  • get_schema_info: Retrieve database schema information
  • create_table, alter_table: Manage database tables
  • export_table_data, import_table_data: Migrate data between formats
  • copy_between_databases: Copy data between PostgreSQL instances
  • monitor_database: Real-time monitoring of PostgreSQL metrics

Usage Examples in Cursor AI

  • "Analyze my PostgreSQL database at postgresql://user:password@localhost:5432/dbname"
  • "Show me the schema for the users table in my PostgreSQL database"
  • "Create a new products table with id, name, price, and created_at columns"
  • "Monitor my database performance and show me any queries taking longer than 30 seconds"

2. Obsidian MCP Server

A server that enables AI interaction with Obsidian.md notes and vaults through the Local REST API plugin. This allows you to search, read, and write content in your Obsidian vault directly from Cursor AI.

  • Location : ./obsidian-mcp/
  • Features : List files, search notes, read and write content
  • Source : MarkusPfundstein/mcp-obsidian
  • Requires : Obsidian with the Local REST API plugin installed

Main Tools

  • list_files_in_vault: List all files in your Obsidian vault
  • list_files_in_dir: List files in a specific directory
  • get_file_contents: Get the content of a specific note
  • simple_search: Search your vault for text matches
  • complex_search: Advanced search with pattern matching
  • patch_content: Insert content into existing notes
  • append_content: Add content to new or existing notes
  • batch_get_file_contents: Retrieve multiple files at once

Usage Examples in Cursor AI

  • "List all files in my Obsidian vault"
  • "Get the content of my note called 'Project Requirements'"
  • "Search my vault for all mentions of 'machine learning'"
  • "Create a new note called 'Meeting Summary' with a summary of the latest project meeting"
  • "Add today's tasks to my 'Daily Notes' file under the heading 'Tasks'"

Setup Instructions

Prerequisites

  • Node.js 18 or higher
  • Python 3.11 or higher with pip
  • Obsidian.md with Local REST API plugin installed (for Obsidian integration)
  • PostgreSQL database (for PostgreSQL integration)

PostgreSQL Server Setup

  1. Navigate to the PostgreSQL MCP directory:

    cd postgresql-mcp

  2. Install dependencies and build:

    npm install

npm run build
  1. The server will be built to build/index.js

Obsidian Server Setup

  1. Install the Local REST API plugin in Obsidian:
* Open Obsidian settings → Community plugins
* Browse for "Local REST API" and install
* Enable the plugin and copy the API key from settings
  1. Navigate to the Obsidian MCP directory:

    cd obsidian-mcp

  2. Install the package in development mode:

    pip install -e .

  3. Set your API key in .env file or in the MCP configuration

Configuration for Cursor AI

Edit your Cursor AI MCP configuration file at ~/.cursor/mcp.json:

{
  "mcpServers": {
    "postgresql-mcp": {
      "command": "node",
      "args": ["/path/to/mcp-servers/postgresql-mcp/build/index.js"],
      "env": {
        "PG_DB_MAP": "{\"db1\":\"postgresql://username:password@hostname:5432/database_name?sslmode=require\",\"analytics\":\"postgresql://analytics_user:[[email protected]](/cdn-cgi/l/email-protection):5432/analytics?sslmode=require\",\"default\":\"db1\"}"
      }
    },
    "mcp-obsidian": {
      "command": "/path/to/python/bin/mcp-obsidian",
      "args": [],
      "env": {
        "OBSIDIAN_API_KEY": "your_api_key_here"
      }
    }
  }
}

Replace paths, database connection details, and API key with your actual values. The PG_DB_MAP environment variable lets you configure multiple database connections and reference them by name.

Debugging

Log Files

  • PostgreSQL MCP logs: ~/Library/Logs/Cursor/mcp-server-postgresql-mcp.log
  • Obsidian MCP logs: ~/Library/Logs/Cursor/mcp-server-mcp-obsidian.log

Using MCP Inspector

For PostgreSQL:

npx @modelcontextprotocol/inspector node /path/to/postgresql-mcp/build/index.js

For Obsidian:

npx @modelcontextprotocol/inspector /path/to/python/bin/mcp-obsidian

Security Considerations

  • PostgreSQL connection strings contain sensitive credentials - use environment variables when possible
  • The Obsidian MCP server has read/write access to your notes - review permissions carefully
  • Both servers run locally on your machine and don't expose services to the network by default

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