MCP Servers for Cursor AI

What is MCP Servers for Cursor AI?

A collection of Model Context Protocol (MCP) server setups that integrate Cursor AI with PostgreSQL databases, Obsidian note vaults, n8n workflow automation, and Google Gemini image generation. Designed for developers using Cursor AI who need tool and database access from their AI assistant.

How to use MCP Servers for Cursor AI?

After satisfying prerequisites (Node.js 18+, Python 3.11+, and required services), navigate to each server directory, install dependencies, and build. Then edit ~/.cursor/mcp.json to add entries for the desired servers, specifying commands, arguments, and environment variables (e.g., PG_DB_MAP, OBSIDIAN_API_KEY, N8N_BASE_URL, GEMINI_API_KEY). Commands run locally and communicate via stdio with Cursor AI.

Key features of MCP Servers for Cursor AI

• Analyze PostgreSQL configuration and performance
• List, search, read, and write Obsidian notes
• List, activate, deactivate, and execute n8n workflows
• Generate images from text via Gemini 2.5 Flash
• Manage database schemas, data migration, and monitoring
• Support for multiple image sizes and customizable output

Use cases of MCP Servers for Cursor AI

• Analyze a PostgreSQL database to find slow queries and schema issues
• Search an Obsidian vault for project notes and append daily tasks
• Activate an n8n workflow and view its execution history
• Generate product‑style images from text descriptions
• Copy data between PostgreSQL instances or import/export tables

FAQ from MCP Servers for Cursor AI

What prerequisites are needed?

Node.js 18 or higher, Python 3.11 or higher with pip, plus the specific service for each server (PostgreSQL database, Obsidian with Local REST API plugin, n8n instance, or Google Gemini API key).

How are credentials and API keys handled?

Connection strings and API keys are set as environment variables in the Cursor AI MCP configuration file (~/.cursor/mcp.json). The README warns never to commit these keys to version control.

Do the servers