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axliupore/mcp-code-runner

by Various

๐Ÿ“‡ ๐Ÿ  - An MCP server for running code locally via Docker and supporting multiple programming languages.

A

MCP

axliupore/mcp-code-runner

Added 1 June 2026

Overview

axliupore/mcp-code-runner is an MCP server that executes code locally inside Docker containers, supporting multiple programming languages. It provides a controlled environment for running snippets or scripts as part of AI agent workflows.

Best for

Best for
Developers building AI agents or tools that need sandboxed multi-language code execution.

Use cases

  • Running user-submitted code snippets safely in an AI chatbot or agent
  • Testing short scripts across multiple languages without setting up local environments
  • Integrating code execution into MCP-based development tools or assistants

Notes

axliupore/mcp-code-runner is an MCP server that executes code locally inside Docker containers, supporting multiple programming languages. It provides a controlled environment for running snippets or scripts as part of AI agent workflows.

18 stars on GitHub. Last updated 2025-04-11. Licensed MIT.

Use cases

  • Running user-submitted code snippets safely in an AI chatbot or agent
  • Testing short scripts across multiple languages without setting up local environments
  • Integrating code execution into MCP-based development tools or assistants

Pros

  • Docker isolation enhances security when running untrusted code
  • Supports a variety of programming languages out of the box
  • Simple MCP protocol integration with existing AI clients

Cons

  • Requires Docker to be installed and running on the host machine
  • Small community and limited stars (18) indicate less testing and support
  • May struggle with large or dependency-heavy code execution tasks

Indexed from awesome-mcp-servers-punkpeye and enriched against its public facts.

Pros

  • Docker isolation enhances security when running untrusted code
  • Supports a variety of programming languages out of the box
  • Simple MCP protocol integration with existing AI clients

Cons

  • Requires Docker to be installed and running on the host machine
  • Small community and limited stars (18) indicate less testing and support
  • May struggle with large or dependency-heavy code execution tasks