About MCP Server Template for Cursor IDE
MCP Server Template for Cursor IDE is a starter project for creating custom MCP tools for Cursor. It supports local setup and deployment, with stdio and SSE transport options.
Key features
- 01Custom MCP tool template
- 02Cursor IDE connection
- 03stdio and SSE transports
- 04Docker setup
- 05Environment-based configuration
Use cases
- 01Creating custom tools for Cursor
- 02Running a server locally with stdio
- 03Connecting Cursor to an SSE endpoint
MCP support
The server exposes custom tools to MCP clients. It can connect to Cursor IDE through stdio locally or an SSE endpoint.
Configuration
Local server config is the same for every client. Copy it into your client’s MCP config file.
Tools
Local servers expose tools only once running on your machine, and the maintainer has not provided a tool list.
Details
From the project READMEMCP Server Template for Cursor IDE
A simple template for creating custom tools for Cursor IDE using Model Context Protocol (MCP). Create your own repository from this template, modify the tools, and connect them to your Cursor IDE.

Quick Start
-
Click "Deploy to Heroku" button
-
After deployment, configure Cursor:
- Open Cursor Settings → Features
- Add new MCP server
- Use your Heroku URL with
/ssepath (e.g.,https://<your-app-name>.herokuapp.com/sse)
-
Test your agent's mood in Cursor:
- Ask your agent "Please ask about our server mood and let me know how it is."
- The server will respond with a cheerful message and a heart ❤️
Alternative Setup Methods
You can run the server in three ways: using Docker, traditional Python setup, or directly in Cursor IDE.
Docker Setup
The project includes Docker support for easy deployment:
- Initial setup:
# Clone the repository
git clone https://github.com/kirill-markin/weaviate-mcp-server.git
cd weaviate-mcp-server
# Create environment file
cp .env.example .env
- Build and run using Docker Compose:
# Build and start the server
docker compose up --build -d
# View logs
docker compose logs -f
# Check server status
docker compose ps
# Stop the server
docker compose down
-
The server will be available at:
- SSE endpoint: http://localhost:8000/sse
-
Quick test:
# Test the server endpoint
curl -i http://localhost:8000/sse
- Connect to Cursor IDE:
- Open Cursor Settings → Features
- Add new MCP server
- Type: Select "sse"
- URL: Enter
http://localhost:8000/sse
Traditional Setup
First, install the uv package manager:
# Install uv on macOS
brew install uv
# Or install via pip (any OS)
pip install uv
Start the server using either stdio (default) or SSE transport:
# Install the package with development dependencies
uv pip install -e ".[dev]"
# Using stdio transport (default)
uv run mcp-simple-tool
# Using SSE transport on custom port
uv run mcp-simple-tool --transport sse --port 8000
# Run tests
uv run pytest -v
After installation, you can connect the server directly to Cursor IDE:
- Right-click on the
cursor-run-mcp-server.shfile in Cursor - Select "Copy Path" to copy the absolute path
- Open Cursor Settings (gear icon)
- Navigate to Features tab
- Scroll down to "MCP Servers"
- Click "Add new MCP server"
- Fill in the form:
- Name: Choose any name (e.g., "my-mcp-server-1")
- Type: Select "stdio" (not "sse" because we run the server locally)
- Command: Paste the absolute path to
cursor-run-mcp-server.shthat you copied earlier. For example:/Users/kirillmarkin/weaviate-mcp-server/cursor-run-mcp-server.sh
Environment Variables
Available environment variables (can be set in .env):
MCP_SERVER_PORT(default: 8000) - Port to run the server onMCP_SERVER_HOST(default: 0.0.0.0) - Host to bind the server toDEBUG(default: false) - Enable debug modeMCP_USER_AGENT- Custom User-Agent for website fetching
Additional options
Installing via Smithery
To install MCP Server Template for Cursor IDE for Claude Desktop automatically via Smithery:
npx -y @smithery/cli install @kirill-markin/example-mcp-server --client claude
Glama server review
Comments
New to MCP?
From what the protocol is to installing your first server, these answers cover most first questions.
An open protocol that defines how AI applications connect to external tools and data in a uniform way. Clients talk to the model; MCP servers provide tools, resources and prompt templates. They exchange standard messages, so one server can be reused by many clients.