Is the mcp_code_executor MCP server actively maintained?

The mcp_code_executor MCP server was last committed 1y ago and has 6 contributors. Issue health: 54% of issues resolved. AgentRank freshness score reflects real-time maintenance activity.

mcp_code_executor MCP Server

Name: bazinga012/mcp_code_executor
Rating: 3.2 (1 reviews)
Author: bazinga012

bazinga012/mcp_code_executor

Score: 54.5 Rank #10923 MCP Server

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The MCP Code Executor is an MCP server that allows LLMs to execute Python code within a specified Conda environment.

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Overview

bazinga012/mcp_code_executor is a JavaScript MCP server licensed under MIT. The MCP Code Executor is an MCP server that allows LLMs to execute Python code within a specified Conda environment.

Ranked #10923 out of 24846 indexed tools.

Ecosystem

JavaScript MIT

Score Breakdown

Stars 15% 212

212 stars → growing community

Freshness 25% 1y ago

Last commit 1y ago → stale

Issue Health 25% 54%

7/13 issues closed → decent issue management

Contributors 10% 6

6 contributors → small team

Dependents 25% 0

No dependents → no downstream usage

npm Downloads N/A

PyPI Downloads N/A

Forks 44

Description Good

License MIT

Weights: Freshness 20% · Issue Health 20% · Dependents 22% · Stars 10% · Contributors 8% · How we score →

How to Improve

Description low impact

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Freshness high impact

Last commit was 522 days ago — a recent commit would boost your freshness score

Dependents medium impact

No downstream dependents detected yet — adoption by other projects is the strongest trust signal

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Details

Stars: 212
Forks: 44
Open Issues: 6
Closed Issues: 7
Contributors: 6
Dependents: —
Language: JavaScript
License: MIT
Last Commit: 1y ago

Matched Queries

"mcp server""mcp-server"

From the README

# 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"></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://gi

Read full README on GitHub →