"Python Code for Data Analysis" matching MCP servers:

An MCP server for securely executing Python code, supporting STDIO, Streamable HTTP, and SSE transports, with easy deployment on Heroku.

Enables execution of Python code in a safe environment, including running scripts, installing packages, and retrieving variable values. Supports file operations and package management through pip.

Local MCP server that registers restricted Python filters and runs them against local JSON, YAML, and TXT files, enabling safe data filtering and file conversion.

Sandboxed Python execution with automatic dependency management. Executes Python scripts in isolated environments (bubblewrap or Docker) with PEP 723 inline dependencies, preventing host pollution.

Enables AI coding assistants to browse Unreal Python API documentation, search classes and functions, and execute Python code in Unreal Editor.

Enables a model to write short Python programs that call a wrapped MCP server's tools as async functions, rather than invoking them one at a time. The program runs in a locked-down Docker sandbox, and every tool call passes through a host-side bridge that enforces an allowlist, schema-validated arguments, call budgets, timeouts and output caps.

Universal Python code execution MCP server that lets LLMs write and run Python for any task, with auto-install packages, streaming output, and automatic file display.

MCP server for E2B code sandbox, enabling creation, management, and execution of code in secure sandboxes using Python and shell commands.

An MCP server that exposes Python debugging tools backed by debugpy, providing a focused debugging surface for local scripts.

Enables AI assistants to execute code in isolated sandboxes with support for Python, JavaScript, and TypeScript, featuring intelligent caching and semantic search for code reuse.

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.

Executes Python code in isolated rootless containers while proxying MCP server tools, reducing context overhead by 95%+ and enabling complex multi-tool workflows through sandboxed code execution.

Provides a sandboxed Docker environment for executing Python code against API endpoints, exposing tools for discovery, inspection, execution, and reuse.

Execute Python code via subprocess with timeout and error handling, returning stdout, stderr, and exit code.

Allows LLMs to execute Python code in a specified Conda environment, enabling access to necessary libraries and dependencies for efficient code execution.

A lightweight Model Context Protocol (MCP) orchestrator designed for efficiency at scale. It features TOON compression (reducing token usage by 30-90%) and Lazy Loading, making it the ideal solution for complex, multi-tool agentic workflows.

Enables LLMs to interact with Python environments, execute code, manage files, and handle packages through the Model Context Protocol.

An MCP server that provides secure code execution capabilities using AWS Bedrock AgentCore's CodeInterpreter, supporting Python, JavaScript, TypeScript, shell commands, and file operations.

Enables Large Language Models to access and manipulate Autodesk Revit models through a pyRevit-based bridge and the Model Context Protocol.

Enables Claude Code to delegate tasks to OpenAI's Codex CLI (GPT-5.4) with structured execution traces, parallel execution, session persistence, and adversarial code review.