MCP Code Executor
by bazinga012
The MCP Code Executor is an MCP server that allows LLMs to execute Python code within a specified Conda environment. This enables LLMs to run code with access to libraries and dependencies defined in the Conda environment.
Last updated: N/A
What is MCP Code Executor?
The MCP Code Executor is a server designed to enable Large Language Models (LLMs) to execute Python code. It provides a secure and isolated environment using Conda, allowing LLMs to leverage external libraries and dependencies.
How to use MCP Code Executor?
To use the MCP Code Executor, first clone the repository, install the Node.js dependencies, and build the project. Then, configure your MCP servers configuration file with the correct paths to the server's executable, code storage directory, and Conda environment name. Once configured, LLMs can generate and execute code by referencing this MCP server in their prompts.
Key features of MCP Code Executor
Execute Python code from LLM prompts
Run code within a specified Conda environment
Configurable code storage directory
Use cases of MCP Code Executor
Allow LLMs to perform complex calculations
Enable LLMs to interact with external APIs
Facilitate data analysis and manipulation by LLMs
Provide LLMs with access to specialized libraries
FAQ from MCP Code Executor
What is an MCP server?
What is an MCP server?
MCP (Meta-Control Protocol) servers are components that extend the capabilities of LLMs by providing specific functionalities. In this case, code execution.
Why use a Conda environment?
Why use a Conda environment?
Conda environments provide isolated spaces for Python projects, ensuring that dependencies don't conflict and that the code runs consistently.
How do I specify the Conda environment?
How do I specify the Conda environment?
You specify the Conda environment name in the CONDA_ENV_NAME environment variable within the MCP server configuration.
Where is the executed code stored?
Where is the executed code stored?
The executed code is stored in the directory specified by the CODE_STORAGE_DIR environment variable.
What if I encounter issues during setup?
What if I encounter issues during setup?
Please refer to the repository's issue tracker or submit a pull request with your contributions.