URL: https://gofastmcp.com/integrations/cursor
Title: Cursor 🤝 FastMCP - FastMCP

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Requirements
Create a Server
Install the Server
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Workspace Installation
Dependencies
Python Version and Project Configuration
Environment Variables
Generate MCP JSON
Manual Configuration
Dependencies
Environment Variables
Using the Server
AI Assistants
Cursor 🤝 FastMCP
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Install and use FastMCP servers in Cursor
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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.
​
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.
​
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
()
​
Install the Server
​
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
​
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:
​
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/public/schemas/fastmcp.json/v1.json
"
,
"
source
"
:
{
"
path
"
:
"
server.py
"
,
"
entrypoint
"
:
"
mcp
"
},
"
environment
"
:
{
"
dependencies
"
:
[
"
pandas
"
,
"
requests
"
]
}
}
​
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
​
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.
​
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.
​
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.
​
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.
​
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.
​
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.
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