This integration focuses on running local FastMCP server files with STDIO transport. For remote servers running with HTTP or SSE transport, use your client's native configuration - FastMCP's integrations focus on simplifying the complex local setup with dependencies and uv commands.
Cursor supports MCP servers through multiple transport methods including STDIO, SSE, and Streamable HTTP, allowing you to extend Cursor’s AI assistant with custom tools, resources, and prompts from your FastMCP servers.

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Requirements

This integration uses STDIO transport to run your FastMCP server locally. For remote deployments, you can run your FastMCP server with HTTP or SSE transport and configure it directly in Cursor’s settings.

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Create a Server

The examples in this guide will use the following simple dice-rolling server, saved as server.py.
server.py
import random
from fastmcp import FastMCP

mcp = FastMCP(name="Dice Roller")

@mcp.tool
def roll_dice(n_dice: int) -> list[int]:
    """Roll `n_dice` 6-sided dice and return the results."""
    return [random.randint(1, 6) for _ in range(n_dice)]

if __name__ == "__main__":
    mcp.run()

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Install the Server

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FastMCP CLI

New in version: 2.10.3 The easiest way to install a FastMCP server in Cursor is using the fastmcp install cursor command. This automatically handles the configuration, dependency management, and opens Cursor with a deeplink to install the server. 
fastmcp install cursor server.py

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Workspace Installation

New in version: 2.12.0 By default, FastMCP installs servers globally for Cursor. You can also install servers to project-specific workspaces using the --workspace flag: 
# Install to current directory's .cursor/ folder
fastmcp install cursor server.py --workspace .

# Install to specific workspace
fastmcp install cursor server.py --workspace /path/to/project
This creates a .cursor/mcp.json configuration file in the specified workspace directory, allowing different projects to have their own MCP server configurations. The install command supports the same file.py:object notation as the run command. If no object is specified, it will automatically look for a FastMCP server object named mcp, server, or app in your file: 
# These are equivalent if your server object is named 'mcp'
fastmcp install cursor server.py
fastmcp install cursor server.py:mcp

# Use explicit object name if your server has a different name
fastmcp install cursor server.py:my_custom_server
After running the command, Cursor will open automatically and prompt you to install the server. The command will be uv, which is expected as this is a Python STDIO server. Click “Install” to confirm: Cursor install prompt

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Dependencies

FastMCP offers multiple ways to manage dependencies for your Cursor servers: Individual packages: Use the --with flag to specify packages your server needs. You can use this flag multiple times: 
fastmcp install cursor server.py --with pandas --with requests
Requirements file: For projects with a requirements.txt file, use --with-requirements to install all dependencies at once: 
fastmcp install cursor server.py --with-requirements requirements.txt
Editable packages: When developing local packages, use --with-editable to install them in editable mode: 
fastmcp install cursor server.py --with-editable ./my-local-package
Alternatively, you can use a fastmcp.json configuration file (recommended):
fastmcp.json
{
  "$schema": "https://gofastmcp.com/schemas/fastmcp_config/v1.json",
  "entrypoint": {
    "file": "server.py",
    "object": "mcp"
  },
  "environment": {
    "dependencies": ["pandas", "requests"]
  }
}

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Python Version and Project Configuration

Control your server’s Python environment with these options: Python version: Use --python to specify which Python version your server should use. This is essential when your server requires specific Python features: 
fastmcp install cursor server.py --python 3.11
Project directory: Use --project to run your server within a specific project context. This ensures uv discovers all project configuration files and uses the correct virtual environment: 
fastmcp install cursor server.py --project /path/to/my-project

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Environment Variables

Cursor runs servers in a completely isolated environment with no access to your shell environment or locally installed applications. You must explicitly pass any environment variables your server needs.
If your server needs environment variables (like API keys), you must include them: 
fastmcp install cursor server.py --server-name "Weather Server" \
  --env API_KEY=your-api-key \
  --env DEBUG=true
Or load them from a .env file: 
fastmcp install cursor server.py --server-name "Weather Server" --env-file .env
uv must be installed and available in your system PATH. Cursor runs in its own isolated environment and needs uv to manage dependencies.

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Generate MCP JSON

Use the first-class integration above for the best experience. The MCP JSON generation is useful for advanced use cases, manual configuration, or integration with other tools.
You can generate MCP JSON configuration for manual use: 
# Generate configuration and output to stdout
fastmcp install mcp-json server.py --server-name "Dice Roller" --with pandas

# Copy configuration to clipboard for easy pasting
fastmcp install mcp-json server.py --server-name "Dice Roller" --copy
This generates the standard mcpServers configuration format that can be used with any MCP-compatible client.

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Manual Configuration

For more control over the configuration, you can manually edit Cursor’s configuration file. The configuration file is located at:
  • All platforms: ~/.cursor/mcp.json
The configuration file is a JSON object with a mcpServers key, which contains the configuration for each MCP server. 
{
  "mcpServers": {
    "dice-roller": {
      "command": "python",
      "args": ["path/to/your/server.py"]
    }
  }
}
After updating the configuration file, your server should be available in Cursor.

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Dependencies

If your server has dependencies, you can use uv or another package manager to set up the environment. When manually configuring dependencies, the recommended approach is to use uv with FastMCP. The configuration should use uv run to create an isolated environment with your specified packages: 
{
  "mcpServers": {
    "dice-roller": {
      "command": "uv",
      "args": [
        "run",
        "--with", "fastmcp",
        "--with", "pandas",
        "--with", "requests", 
        "fastmcp",
        "run",
        "path/to/your/server.py"
      ]
    }
  }
}
You can also manually specify Python versions and project directories in your configuration: 
{
  "mcpServers": {
    "dice-roller": {
      "command": "uv",
      "args": [
        "run",
        "--python", "3.11",
        "--project", "/path/to/project",
        "--with", "fastmcp",
        "fastmcp",
        "run",
        "path/to/your/server.py"
      ]
    }
  }
}
Note that the order of arguments is important: Python version and project settings should come before package specifications.
uv must be installed and available in your system PATH. Cursor runs in its own isolated environment and needs uv to manage dependencies.

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Environment Variables

You can also specify environment variables in the configuration: 
{
  "mcpServers": {
    "weather-server": {
      "command": "python",
      "args": ["path/to/weather_server.py"],
      "env": {
        "API_KEY": "your-api-key",
        "DEBUG": "true"
      }
    }
  }
}
Cursor runs servers in a completely isolated environment with no access to your shell environment or locally installed applications. You must explicitly pass any environment variables your server needs.

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Using the Server

Once your server is installed, you can start using your FastMCP server with Cursor’s AI assistant. Try asking Cursor something like:
“Roll some dice for me”
Cursor will automatically detect your roll_dice tool and use it to fulfill your request, returning something like:
🎲 Here are your dice rolls: 4, 6, 4 You rolled 3 dice with a total of 14! The 6 was a nice high roll there!
The AI assistant can now access all the tools, resources, and prompts you’ve defined in your FastMCP server.