Mcp Code Executor

What is Mcp Code Executor about?

Mcp Code Executor enables large language models to run Python code in a chosen environment (Conda, standard virtualenv, or UV‑based virtualenv). It creates and stores code files, installs required packages, and executes the code while keeping the environment isolated.

How to use Mcp Code Executor?

1. Clone the repository and install Node.js dependencies.

   git clone https://github.com/bazinga012/mcp_code_executor.git
   cd mcp_code_executor
   npm install
   npm run build
   

2. Configure the server in your MCP configuration file. Example for a Conda environment:

   {
     "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": "my-conda-env"
         }
       }
     }
   }
   

   For Docker, replace the command with docker and provide the appropriate args as shown in the README.
3. Call the provided tools (execute_code, install_dependencies, initialize_code_file, etc.) from an LLM prompt. The server will handle file creation, dependency installation, and execution.

Key Features

• Execute arbitrary Python snippets directly from LLM prompts.
• Incremental code generation: initialize a file, append more code, then execute—ideal for large scripts.
• Support for Conda, standard virtualenv, and UV‑based virtualenv environments.
• Automatic dependency installation and package‑existence checks.
• Runtime environment re‑configuration via configure_environment.
• Configurable code storage directory for persisted scripts.

Use Cases

• Data analysis: LLM generates pandas/numpy code, installs missing libraries, and runs the analysis.
• Prototype development: Quickly test small Python utilities without leaving the chat interface.
• Educational platforms: Students write and run code snippets in a controlled environment.
• Automation scripts: Generate and execute multi‑step scripts that exceed token limits using the incremental workflow.

FAQ

Q: Which environments are supported? A: Conda, standard Python virtualenv, and UV‑based virtualenv. Choose by setting ENV_TYPE and the corresponding path variables.

Q: Do I need internet access for dependency installation? A: Yes, the server uses pip (or conda) to fetch packages from public repositories.

Q: How does the server handle large code blocks? A: Use initialize_code_file to create a base file, then append_to_code_file for additional sections, and finally execute_code_file to run the complete script.

Q: Can I change the environment after the server has started? A: Yes, call configure_environment with the new settings; the server will reload the configuration.

Q: Where are the generated scripts stored? A: In the directory defined by the CODE_STORAGE_DIR environment variable.

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

1. Clone this repository:

git clone https://github.com/bazinga012/mcp_code_executor.git

2. Navigate to the project directory:

cd mcp_code_executor

3. Install the Node.js dependencies:

npm install

4. 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:

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.