Python Repl
БесплатноНе проверенA production-grade MCP server providing a persistent Python REPL with multi-session support, sandboxing, and timeout protection, enabling LLM agents to execute
Описание
A production-grade MCP server providing a persistent Python REPL with multi-session support, sandboxing, and timeout protection, enabling LLM agents to execute Python code across multiple turns with variables that persist between calls.
README
A production-grade MCP server providing a persistent Python REPL with multi-session support, sandboxing, and timeout protection.
Built for LLM agents that need to execute Python code across multiple turns with variables that persist between calls.
✨ Features
| Feature | Description |
|---|---|
| Multi-session | Isolated sessions with unique IDs — run parallel workflows |
| Persistent namespace | Variables survive across calls within a session |
| Timeout protection | Configurable execution timeout (SIGALRM on Unix) |
| Sandboxing | Optional mode blocks dangerous modules (subprocess, socket, etc.) |
| Package install | Install pip packages on-the-fly (prefers uv for speed) |
| File execution | Run .py files inside the persistent session |
| Dual transport | stdio (local) and streamable-http (remote) |
| Full introspection | List variables, get history, check server status |
| Env-based config | All settings via REPL_* environment variables |
🚀 Quick Start
With Claude Desktop / Cursor (stdio)
Add to your MCP config:
{
"mcpServers": {
"python-repl": {
"command": "uvx",
"args": ["mcp-python-repl"]
}
}
}
With uv (local dev)
# Clone and run
git clone https://github.com/soufiane-aazizi/mcp-python-repl.git
cd mcp-python-repl
uv run mcp-python-repl
HTTP transport (remote / multi-client)
REPL_TRANSPORT=streamable-http REPL_PORT=8000 uv run mcp-python-repl
🛠️ Tools
Code Execution
| Tool | Description |
|---|---|
repl_run_code |
Execute Python code with persistent namespace |
repl_run_file |
Execute a .py file in the session |
repl_install_package |
Install a pip package (uses uv if available) |
Namespace Management
| Tool | Description |
|---|---|
repl_list_namespace |
List all variables in a session |
repl_get_variable |
Get the full value of a variable |
repl_set_variable |
Inject a variable from JSON |
repl_delete_variable |
Delete a specific variable |
repl_clear_namespace |
Clear all variables in a session |
Session Management
| Tool | Description |
|---|---|
repl_list_sessions |
List all active sessions |
repl_delete_session |
Delete a session and its data |
Debugging
| Tool | Description |
|---|---|
repl_get_history |
Get execution history for a session |
repl_server_status |
Server config, Python version, session count |
🔄 How Persistence Works
Call 1: repl_run_code(code="data = [1,2,3]; total = sum(data); result = total")
→ returns: {"result": 6, "session_id": "a1b2c3d4e5f6", "new_variables": ["data", "total"]}
Call 2: repl_run_code(code="doubled = [x*2 for x in data]; result = doubled", session_id="a1b2c3d4e5f6")
→ returns: {"result": [2,4,6], "new_variables": ["doubled"]}
Important: The
resultvariable is for returning output to the caller. It does NOT persist. Use named variables instead.
⚙️ Configuration
All settings are configurable via environment variables:
| Variable | Default | Description |
|---|---|---|
REPL_TIMEOUT |
30 |
Max execution time in seconds |
REPL_MAX_SESSIONS |
50 |
Maximum concurrent sessions |
REPL_SESSION_TTL |
120 |
Session expiry in minutes |
REPL_MAX_OUTPUT |
1048576 |
Max stdout/stderr capture (bytes) |
REPL_SANDBOX |
false |
Enable sandboxing (true/false) |
REPL_TRANSPORT |
stdio |
Transport: stdio or streamable-http |
REPL_HOST |
127.0.0.1 |
HTTP host (when using HTTP transport) |
REPL_PORT |
8000 |
HTTP port (when using HTTP transport) |
REPL_WORKDIR |
cwd |
Working directory for executions |
Sandbox Mode
When REPL_SANDBOX=true, the following modules are blocked:
subprocess, shutil, ctypes, socket, http.server, xmlrpc, ftplib, smtplib, telnetlib, webbrowser
And the following builtins are removed: exec, eval, compile, __import__ (replaced with a restricted version).
🧪 Development
# Install dev dependencies
uv sync --extra dev
# Run tests
uv run pytest -v
# Lint
uv run ruff check src/ tests/
# Test with MCP Inspector
npx @modelcontextprotocol/inspector uv run mcp-python-repl
📦 Project Structure
mcp-python-repl/
├── src/mcp_python_repl/
│ ├── __init__.py # Package metadata
│ ├── config.py # Env-based configuration
│ ├── session.py # Multi-session manager with TTL
│ ├── executor.py # Python code executor (timeout + sandbox)
│ └── server.py # MCP server with all tools
├── tests/
│ └── test_core.py # Unit + integration tests
├── pyproject.toml # uv/hatch project config
├── LICENSE # MIT
└── README.md
📄 License
MIT — See LICENSE.
Установка Python Repl
У этого сервера нет опубликованного пакета — он собирается из исходников. Открой репозиторий и следуй инструкции в README.
▸ github.com/aazizisoufiane/mcp-python-replFAQ
Python Repl MCP бесплатный?
Да, Python Repl MCP бесплатный — установка в пару кликов через Unyly без оплаты.
Нужен ли API-ключ для Python Repl?
Нет, Python Repl работает без API-ключей и переменных окружения.
Python Repl — hosted или self-hosted?
Self-hosted: сервер запускается локально на твоей машине командой из раздела установки.
Как установить Python Repl в Claude Desktop, Claude Code или Cursor?
Открой Python Repl на unyly.org, выбери вкладку своего клиента (Claude Desktop, Claude Code, Cursor) и нажми Install — конфиг сгенерируется автоматически, без правки JSON.
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