Content
# MCP Code Executor
[](https://smithery.ai/server/@bazinga012/mcp_code_executor)
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. It also supports incremental code generation for handling large code blocks that may exceed token limits.
<a href="https://glama.ai/mcp/servers/45ix8xode3"><img width="380" height="200" src="https://glama.ai/mcp/servers/45ix8xode3/badge" alt="Code Executor MCP server" /></a>
## Features
- Execute Python code from LLM prompts
- Support for incremental code generation to overcome token limitations
- 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
## Prerequisites
- Node.js installed
- One of the following:
- Conda installed with desired Conda environment created
- Python virtualenv
- UV virtualenv
## Setup
1. Clone this repository:
```bash
git clone https://github.com/bazinga012/mcp_code_executor.git
```
2. Navigate to the project directory:
```bash
cd mcp_code_executor
```
3. Install the Node.js dependencies:
```bash
npm install
```
4. Build the project:
```bash
npm run build
```
## Configuration
To configure the MCP Code Executor server, add the following to your MCP servers configuration file:
### Using Node.js
```json
{
"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"
}
}
}
}
```
### Using Docker
```json
{
"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.
### Environment Variables
#### Required Variables
- `CODE_STORAGE_DIR`: Directory where the generated code will be stored
#### Environment Type (choose one setup)
- **For Conda:**
- `ENV_TYPE`: Set to `conda`
- `CONDA_ENV_NAME`: Name of the Conda environment to use
- **For Standard Virtualenv:**
- `ENV_TYPE`: Set to `venv`
- `VENV_PATH`: Path to the virtualenv directory
- **For UV Virtualenv:**
- `ENV_TYPE`: Set to `venv-uv`
- `UV_VENV_PATH`: Path to the UV virtualenv directory
## Available Tools
The MCP Code Executor provides the following tools to LLMs:
### 1. `execute_code`
Executes Python code in the configured environment. Best for short code snippets.
```json
{
"name": "execute_code",
"arguments": {
"code": "import numpy as np\nprint(np.random.rand(3,3))",
"filename": "matrix_gen"
}
}
```
### 2. `install_dependencies`
Installs Python packages in the environment.
```json
{
"name": "install_dependencies",
"arguments": {
"packages": ["numpy", "pandas", "matplotlib"]
}
}
```
### 3. `check_installed_packages`
Checks if packages are already installed in the environment.
```json
{
"name": "check_installed_packages",
"arguments": {
"packages": ["numpy", "pandas", "non_existent_package"]
}
}
```
### 4. `configure_environment`
Dynamically changes the environment configuration.
```json
{
"name": "configure_environment",
"arguments": {
"type": "conda",
"conda_name": "new_env_name"
}
}
```
### 5. `get_environment_config`
Gets the current environment configuration.
```json
{
"name": "get_environment_config",
"arguments": {}
}
```
### 6. `initialize_code_file`
Creates a new Python file with initial content. Use this as the first step for longer code that may exceed token limits.
```json
{
"name": "initialize_code_file",
"arguments": {
"content": "def main():\n print('Hello, world!')\n\nif __name__ == '__main__':\n main()",
"filename": "my_script"
}
}
```
### 7. `append_to_code_file`
Appends content to an existing Python code file. Use this to add more code to a file created with initialize_code_file.
```json
{
"name": "append_to_code_file",
"arguments": {
"file_path": "/path/to/code/storage/my_script_abc123.py",
"content": "\ndef another_function():\n print('This was appended to the file')\n"
}
}
```
### 8. `execute_code_file`
Executes an existing Python file. Use this as the final step after building up code with initialize_code_file and append_to_code_file.
```json
{
"name": "execute_code_file",
"arguments": {
"file_path": "/path/to/code/storage/my_script_abc123.py"
}
}
```
### 9. `read_code_file`
Reads the content of an existing Python code file. Use this to verify the current state of a file before appending more content or executing it.
```json
{
"name": "read_code_file",
"arguments": {
"file_path": "/path/to/code/storage/my_script_abc123.py"
}
}
```
## Usage
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.
### Handling Large Code Blocks
For larger code blocks that might exceed LLM token limits, use the incremental code generation approach:
1. **Initialize a file** with the basic structure using `initialize_code_file`
2. **Add more code** in subsequent calls using `append_to_code_file`
3. **Verify the file content** if needed using `read_code_file`
4. **Execute the complete code** using `execute_code_file`
This approach allows LLMs to write complex, multi-part code without running into token limitations.
## Backward Compatibility
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.
## Contributing
Contributions are welcome! Please open an issue or submit a pull request.
## License
This project is licensed under the MIT License.
