python-mcp-server 1.0.1

A world-class Python interpreter MCP server with session management, health monitoring, and FastMCP middleware

Python Interpreter MCP Server

A world-class Python interpreter MCP (Model Context Protocol) server that provides secure, isolated Python code execution with advanced session management and health monitoring. Built with FastMCP for production-grade reliability.

✨ Features

🚀 Production-Ready Architecture

• FastMCP Middleware Stack: Professional error handling, timing, and contextual logging
• Session-Based Isolation: Multiple independent Python kernels with complete state separation
• Health Monitoring: Real-time kernel diagnostics with intelligent restart capabilities
• Progressive Timeouts: Resilient execution with smart retry logic

🔒 Security & Reliability

• Workspace Sandboxing: Path traversal protection and secure file operations
• Resource Monitoring: CPU, memory, and process health tracking
• State Preservation: Automatic kernel state backup and restoration
• Error Resilience: Graceful handling of kernel failures and timeouts

🛠 Developer Experience

• Notebook Surface: One tool to run cells, manage datasets, and export artifacts
• File Management: Read, write, delete operations with safety checks
• Package Installation: Dynamic dependency management with uv/pip

🎯 FastMCP Integration

• Native FastMCP Configuration: Built-in fastmcp.json for easy deployment
• CLI Integration: Works seamlessly with FastMCP CLI commands
• Multiple Transport Modes: STDIO, HTTP, Server-Sent Events support
• Claude Desktop Ready: Perfect integration with Claude Desktop

🚀 Quick Start

Installation

Method 1: PyPI (Recommended)

pip install python-mcp-server

Method 2: uvx (Isolated)

uvx install python-mcp-server

Method 3: FastMCP CLI (For Claude Desktop)

fastmcp install claude-desktop python-mcp-server

Basic Usage

Standalone Server (HTTP Mode)

# Start HTTP server
python-mcp-server --host 127.0.0.1 --port 8000

# Custom workspace
python-mcp-server --workspace /path/to/workspace --port 8000

FastMCP CLI (Recommended)

# Run with FastMCP (auto-detects fastmcp.json)
fastmcp run

# Run with specific transport
fastmcp run --transport http --port 8000

# For development/testing
fastmcp dev

Client Usage

FastMCP Client

import asyncio
from fastmcp.client import Client

async def main():
    async with Client("http://localhost:8000/mcp") as client:
        # Execute Python code
        result = await client.call_tool("run_python_code", {
            "code": """
import numpy as np
import matplotlib.pyplot as plt

x = np.linspace(0, 10, 100)
y = np.sin(x)

plt.figure(figsize=(10, 6))
plt.plot(x, y, 'b-', linewidth=2)
plt.title('Sine Wave')
plt.grid(True)
plt.show()

print("Plot created successfully!")
"""
        })
        print("Output:", result.data["stdout"])

asyncio.run(main())

🏗 Architecture

graph TB
    A[MCP Client] --> B[FastMCP Server]
    B --> C[Session Manager]
    C --> D[Kernel Session 1]
    C --> E[Kernel Session 2]
    C --> F[Default Session]
    
    B --> G[Health Monitor]
    G --> H[Resource Tracker]
    G --> I[Timeout Handler]
    
    B --> J[Workspace Manager]
    J --> K[File Operations]
    J --> L[Security Layer]

📦 Package Structure (v1.0.0)

src/python_mcp_server/
├── server.py          # Main FastMCP server implementation
├── __init__.py         # Package initialization
└── ...

fastmcp.json           # FastMCP configuration
pyproject.toml         # Package metadata and dependencies
tests/                 # Comprehensive test suite
docs/                  # Documentation source

🔧 Tools Reference

Notebook (single surface)

notebook(action=..., ...) — one tool for cells, datasets, and exports.

• Actions:
  • run — execute a cell; capture stdout/stderr, figures (PNG/SVG), JSON outputs, and a workspace diff. Writes a manifest per cell under outputs/notebooks/<id>/.
  • cells — list executed cells (from index.json).
  • cell — fetch one cell’s manifest.
  • export — write a .ipynb (and .html if available) to outputs/notebooks/.
  • reset — clear notebook manifests for the same notebook_id (kernel is not restarted).
  • datasets.register — create/update a DuckDB VIEW over CSV/Parquet files or globs.
  • datasets.list — show registered datasets.
  • datasets.describe — schema + head(50).
  • datasets.drop — remove a registered dataset/view.
  • datasets.sql — run SQL across registered views; returns preview and writes full result to Parquet.

Core Execution

• run_python_code - Execute Python with output capture (rich display assets saved to outputs/)
• restart_kernel - Clean kernel restart with optional state preservation

File Operations

• list_files - Browse workspace (flat or tree view)
• read_file / write_file / delete_file - Safe file operations

Package Management

• install_dependencies - Dynamic dependency installation with uv/pip fallback

💡 Advanced Usage

Notes

• Session isolation, health and concurrency controls run under the hood; the public tool surface is intentionally minimal for agent reliability.

🧰 Interpreter / Venv Configuration

The server runs the Jupyter kernel with the same interpreter and environment as the process by default. You can override with flags in both stdio and http modes:

python-mcp-server \
  --python /path/to/python \
  --venv /path/to/venv \
  --pythonpath "/extra/dir1:/extra/dir2" \
  --transport http --port 8000

Environment variables honored: PYTHON, MPLBACKEND=Agg (default), MCP_MAX_CONCURRENT_CELLS, MCP_MEMORY_BUDGET_MB, MCP_SOFT_WATERMARK, MCP_HARD_WATERMARK.

📊 Datasets (DuckDB-backed)

Register CSV/Parquet files (or globs) as DuckDB views and query with SQL:

# Register
await client.call_tool("notebook", {
  "action": "datasets.register",
  "dataset_name": "sales",
  "paths": ["data/sales_*.parquet"],
  "format": "parquet"
})

# Describe
await client.call_tool("notebook", {"action": "datasets.describe", "dataset_name": "sales"})

# Query (preview + full Parquet result on disk)
await client.call_tool("notebook", {
  "action": "datasets.sql",
  "query": "select date, sum(amount) as total from sales group by date order by date"
})

🔧 Configuration

FastMCP Configuration (fastmcp.json)

{
  "$schema": "https://gofastmcp.com/public/schemas/fastmcp.json/v1.json",
  "source": {
    "type": "filesystem",
    "path": "src/python_mcp_server/server.py",
    "entrypoint": "mcp"
  },
  "environment": {
    "type": "uv",
    "python": ">=3.10",
    "dependencies": [
      "fastmcp>=1.0.0",
      "jupyter_client>=8.0.0",
      "matplotlib>=3.7.0",
      "pandas>=2.0.0"
    ],
    "editable": ["."]
  },
  "deployment": {
    "transport": "stdio",
    "log_level": "INFO",
    "env": {
      "MCP_WORKSPACE_DIR": "workspace",
      "MPLBACKEND": "Agg",
      "PYTHONUNBUFFERED": "1"
    }
  }
}

Environment Variables

export MCP_WORKSPACE_DIR="./workspace"    # Workspace directory
export MPLBACKEND="Agg"                   # Matplotlib backend
export PYTHONUNBUFFERED="1"               # Unbuffered output

Command Line Options

python-mcp-server \
    --port 8000 \
    --host 0.0.0.0 \
    --workspace ./custom_workspace

📁 Workspace Structure

workspace/
├── scripts/     # Saved Python scripts
├── outputs/     # Generated files (plots, data, etc.)
├── uploads/     # Uploaded files via HTTP
└── (user files) # Any files created during execution

🤝 Integration Examples

With Claude Desktop (STDIO Mode)

Method 1: Direct Command

{
  "mcpServers": {
    "python-interpreter": {
      "command": "python-mcp-server",
      "args": ["--workspace", "/path/to/workspace"]
    }
  }
}

Method 2: FastMCP CLI (Recommended)

# Install for Claude Desktop
fastmcp install claude-desktop fastmcp.json --name "Python Interpreter"

This generates the correct configuration automatically.

Method 3: Direct Python Path

{
  "mcpServers": {
    "python-interpreter": {
      "command": "python", 
      "args": [
        "/path/to/src/python_mcp_server/server.py",
        "--workspace", "/path/to/workspace"
      ]
    }
  }
}

With Claude Code

fastmcp install claude-code fastmcp.json --name "Python Interpreter"

With Cursor

fastmcp install cursor fastmcp.json --name "Python Interpreter"

Custom HTTP Client

# Raw HTTP/JSON-RPC
import httpx

async def call_tool(tool_name, arguments):
    async with httpx.AsyncClient() as client:
        response = await client.post("http://localhost:8000/mcp", json={
            "jsonrpc": "2.0",
            "method": "tools/call",
            "params": {"name": tool_name, "arguments": arguments},
            "id": 1
        })
        return response.json()

With uvx (No Installation)

# Run directly without installing globally
uvx python-mcp-server --port 8000

# With additional dependencies
uvx --with pandas --with numpy python-mcp-server --port 8000

🧪 Development

Setup

git clone https://github.com/deadmeme5441/python-mcp-server.git
cd python-mcp-server
uv sync

Testing

# Run all tests
uv run pytest tests/ -v

# Run specific test
uv run pytest tests/test_sessions.py -v

# With coverage
uv run pytest --cov=src --cov-report=html

Code Quality

# Linting
uvx ruff@latest check .

# Type checking
uv run mypy src/

# Formatting
uvx ruff@latest format .

FastMCP Development

# Run with FastMCP for development
fastmcp dev

# Test different transports
fastmcp run --transport http --port 8000
fastmcp run --transport stdio

📚 Documentation

• Complete Documentation - Full guides and API reference
• Getting Started - Installation and first steps
• Architecture - Technical deep dive
• Examples - Practical usage patterns
• Troubleshooting - Common issues and solutions

🔒 Security

• Workspace Sandboxing: All operations restricted to configured workspace
• Path Validation: Protection against directory traversal attacks
• Resource Limits: Configurable memory and CPU constraints
• Process Isolation: Each session runs in isolated Jupyter kernel
• Input Sanitization: All inputs validated via Pydantic models

🚀 Use Cases

• AI Agent Environments: Reliable Python execution for AI agents
• Educational Platforms: Safe, isolated code execution for learning
• Data Analysis Workflows: Session-based data processing pipelines
• Research & Prototyping: Quick iteration with state preservation
• Jupyter Alternative: MCP-native Python execution environment
• Claude Desktop Integration: Enhanced Python capabilities for Claude

📋 Changelog (v0.6.0)

🎉 Major Changes

• New Package Structure: Migrated to src/python_mcp_server/ layout
• FastMCP Integration: Added native fastmcp.json configuration
• Fixed CLI Command: python-mcp-server now works correctly
• Enhanced Installation: Multiple installation methods supported

🔧 Improvements

• Better Testing: Fixed all test imports and module structure
• Documentation: Updated for new package structure
• Claude Desktop: Improved integration and configuration
• Code Quality: Full linting compliance and type checking

🐛 Bug Fixes

• Entry Point: Fixed package entry point configuration
• Module Imports: Resolved import issues in tests
• Build System: Proper hatchling configuration

🤝 Contributing

We welcome contributions! Please see our Contributing Guide for details.

1. Fork the repository
2. Create your feature branch: git checkout -b feature/amazing-feature
3. Commit your changes: git commit -m 'Add amazing feature'
4. Push to the branch: git push origin feature/amazing-feature
5. Open a Pull Request

📄 License

This project is licensed under the MIT License - see the LICENSE file for details.

🙏 Acknowledgments

• FastMCP - Excellent MCP framework
• Jupyter - Kernel architecture inspiration
• Model Context Protocol - Protocol specification
• uv - Fast Python package manager

🔗 Links

• PyPI: python-mcp-server
• Documentation: deadmeme5441.github.io/python-mcp-server
• Issues: GitHub Issues
• Discussions: GitHub Discussions

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