ForeverVM MCP Server for Claude Python REPL

Overview

The ForeverVM MCP Server provides a compact Model Context Protocol (MCP) integration that lets Claude (Anthropic) execute Python code inside a managed REPL. It exposes a pair of tools that create and drive Python REPL sessions, enabling interactive code execution for experimentation, debugging, and small-scale programmatic workflows directly from a Claude-enabled client.

This server is designed for low-latency interactive execution during development and testing. It is not intended to replace production-grade execution environments: treat it as a fast developer-facing bridge that runs Python snippets in isolated REPLs and returns results to the calling MCP client.

Features

• Fast, interactive Python REPL creation and execution via MCP-compatible tools
• Two simple tools: create a REPL and run code in a REPL
• Minimal footprint: implemented as a small JavaScript MCP server
• Simple local development setup for integrating with Claude Desktop or other MCP clients
• Intended for testing, debugging, and prototyping code execution workflows

Installation / Configuration

Quickstart (Claude Desktop)

# Install MCP tools in Claude Desktop (uses npx)
npx forevervm-mcp install --claude

Local development / custom MCP client

1. Clone or place the MCP server code on your machine (example path: ./javascript/mcp-server).
2. Configure your MCP client to run the server using npm. Set the client command to npm and the arguments to:

["--prefix", "<path/to/this/directory>", "run", "start", "run"]

Replace <path/to/this/directory> with the path to the MCP server folder.

Notes:

• The provided local setup is intended for development and testing only. Do not expose the server to the public internet without applying proper sandboxing, authentication, and resource controls.
• See the ForeverVM docs for additional MCP client integrations: https://forevervm.com/docs/guides/forevervm-mcp-server/

Available Tools

The MCP server exposes two tools for controlling Python REPL sessions. These are intended to be consumed by MCP-aware clients such as Claude.

Tool overview table

Details

• create-python-repl

  • What it does: Spawns a new Python REPL instance and returns its identifier. Use this ID for subsequent code execution calls.
  • Return: A string identifier (replId) that represents the REPL.

• run-python-in-repl

  • What it does: Executes a given Python code snippet inside the specified REPL and returns the result/output. This supports short-lived commands and REPL-style interactions.
  • Required inputs:
    • code (string): The Python code to execute.
    • replId (string): The identifier of the REPL where the code will run.
  • Return: A result object or textual output indicating stdout, stderr, and execution responses.

Example usage (conceptual payload)

{
  "tool": "run-python-in-repl",
  "input": {
    "replId": "repl-abc123",
    "code": "print('hello from Claude')"
  }
}

Use Cases

• Interactive debugging
  • Ask Claude to reproduce a bug or prototype a function; create a REPL, send the code, and iterate quickly.
• Teaching and exploration
  • Run short Python examples or experiments while working through tutorials or unit tests.
• Rapid prototyping
  • Evaluate small algorithm snippets or data transformations without setting up a full runtime environment.
• Automated small-scale tests
  • Use Claude to drive test snippets against a helper REPL session during development.

Concrete example workflow

1. create-python-repl → returns replId “repl-01”.
2. run-python-in-repl with replId “repl-01” and code:

   import math
   math.factorial(5)
   

3. Receive the result (e.g., “120”) and continue iterating in the same REPL session for stateful interactions.

Security & Operational Notes

• The MCP server is intended for local development and controlled environments. If you must deploy it in shared or production environments:
  • Enforce authentication and authorization on the MCP client/server transport.
  • Restrict network access to trusted clients.
  • Run REPLs in isolated sandboxes or containers and apply resource/time limits to prevent runaway processes.
  • Sanitize inputs and consider execution-time and memory caps.
• For high-volume or production code execution, prefer dedicated, hardened execution services rather than a lightweight REPL-based MCP server.

Resources

• GitHub repository: https://github.com/jamsocket/forevervm/tree/main/javascript/mcp-server
• ForeverVM MCP docs: https://forevervm.com/docs/guides/forevervm-mcp-server/

The ForeverVM MCP Server offers a simple, developer-friendly way to let Claude run Python snippets through a managed REPL. Use it for testing, rapid prototyping, and interactive experimentation while keeping security and scope considerations in mind.