The MCP Code Executor is an MCP server that allows LLMs to execute Python code within a specified Python environment. This enables LLMs to run code with access to libraries and dependencies defined in the environment.
- Execute Python code from LLM prompts
- Run code within a specified environment (Conda, virtualenv, or UV virtualenv)
- Install dependencies when needed
- Check if packages are already installed
- Dynamically configure the environment at runtime
- Configurable code storage directory
- Node.js installed
- One of the following:
- Conda installed with desired Conda environment created
- Python virtualenv
- UV virtualenv
- Clone this repository:
git clone https://github.com/bazinga012/mcp_code_executor.git- Navigate to the project directory:
cd mcp_code_executor- Install the Node.js dependencies:
npm install- Build the project:
npm run buildTo configure the MCP Code Executor server, add the following to your MCP servers configuration file:
{
"mcpServers": {
"mcp-code-executor": {
"command": "node",
"args": [
"/path/to/mcp_code_executor/build/index.js"
],
"env": {
"CODE_STORAGE_DIR": "/path/to/code/storage",
"ENV_TYPE": "conda",
"CONDA_ENV_NAME": "your-conda-env"
}
}
}
}{
"mcpServers": {
"mcp-code-executor": {
"command": "docker",
"args": [
"run",
"-i",
"--rm",
"mcp-code-executor"
]
}
}
}Note: The Dockerfile has been tested with the venv-uv environment type only. Other environment types may require additional configuration.
CODE_STORAGE_DIR: Directory where the generated code will be stored
-
For Conda:
ENV_TYPE: Set tocondaCONDA_ENV_NAME: Name of the Conda environment to use
-
For Standard Virtualenv:
ENV_TYPE: Set tovenvVENV_PATH: Path to the virtualenv directory
-
For UV Virtualenv:
ENV_TYPE: Set tovenv-uvUV_VENV_PATH: Path to the UV virtualenv directory
The MCP Code Executor provides the following tools to LLMs:
Executes Python code in the configured environment.
{
"name": "execute_code",
"arguments": {
"code": "import numpy as np\nprint(np.random.rand(3,3))",
"filename": "matrix_gen"
}
}Installs Python packages in the environment.
{
"name": "install_dependencies",
"arguments": {
"packages": ["numpy", "pandas", "matplotlib"]
}
}Checks if packages are already installed in the environment.
{
"name": "check_installed_packages",
"arguments": {
"packages": ["numpy", "pandas", "non_existent_package"]
}
}Dynamically changes the environment configuration.
{
"name": "configure_environment",
"arguments": {
"type": "conda",
"conda_name": "new_env_name"
}
}Gets the current environment configuration.
{
"name": "get_environment_config",
"arguments": {}
}Once configured, the MCP Code Executor will allow LLMs to execute Python code by generating a file in the specified CODE_STORAGE_DIR and running it within the configured environment.
LLMs can generate and execute code by referencing this MCP server in their prompts.
This package maintains backward compatibility with earlier versions. Users of previous versions who only specified a Conda environment will continue to work without any changes to their configuration.
Contributions are welcome! Please open an issue or submit a pull request.
This project is licensed under the MIT License.