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.

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

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 build

Configuration

To configure the MCP Code Executor server, add the following to your MCP servers configuration file:

Using Node.js

{
  "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

{
  "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.

{
  "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.

{
  "name": "install_dependencies",
  "arguments": {
    "packages": ["numpy", "pandas", "matplotlib"]
  }
}

3. `check_installed_packages`

Checks if packages are already installed in the environment.

{
  "name": "check_installed_packages",
  "arguments": {
    "packages": ["numpy", "pandas", "non_existent_package"]
  }
}

4. `configure_environment`

Dynamically changes the environment configuration.

{
  "name": "configure_environment",
  "arguments": {
    "type": "conda",
    "conda_name": "new_env_name"
  }
}

5. `get_environment_config`

Gets the current environment configuration.

{
  "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.

{
  "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.

{
  "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.

{
  "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.

{
  "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:

Initialize a file with the basic structure using initialize_code_file
Add more code in subsequent calls using append_to_code_file
Verify the file content if needed using read_code_file
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.

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